Mlops jobs

Hiring: AI / ML / DevOps / Python Engineer (Remote) We're looking for a skilled AI / ML / DevOps / Python Engineer to join our team and work on modern AI-powered products and cloud infrastructure. Requirements * 4–5+ years of professional software engineering experience * Strong Python development skills * Solid experience with AI/ML, LLMs, or MLOps * Hands-on experience with Docker, Kubernetes, CI/CD, and cloud platforms (AWS preferred) * Strong understanding of DevOps and automation * Fluent English communication (written and spoken) * Experience working with U.S.-based companies or distributed international teams is highly preferred * Must be available to attend a live video interview in English as part of the hiring process Nice to Have * Experience with OpenAI, ...

The goal is to enhance our existing AI system so it operates as a truly production-grade platform. Today the core models are live, but our pipelines and governance layers lag behind the growing usage of generative AI. I need someone who has already shipped real-world solutions across the full stack—model training, evaluation, deployment, monitoring, and continuous improvement—and can drop straight into an AWS SageMaker environment. Key focus areas • Model lifecycle: automate versioning, lineage, and rollback using GitLab CI/CD and Prefect orchestration. • Quality & evaluation: set up repeatable LLM evaluation suites, RAG benchmarks, and human-in-the-loop (HITL) review loops so we know precisely how each release performs. • Data layer: tighten integrati...

...deploy your work. Everything is green-field, so you’ll have freedom in approach provided you deliver clean, maintainable code. Must-haves • Deep command of Python for data processing, experimentation, and micro-service development (our core language). • Practical experience with TensorFlow, PyTorch, or a comparable framework, plus a solid grasp of feature engineering, experiment tracking, and MLOps principles. • Comfort with cloud services (AWS, GCP, or Azure) and containerisation so your models ship smoothly. Deliverables 1. End-to-end prototype of at least one core model, complete with well-documented Python code. 2. Concise technical write-up detailing your architecture choices, evaluation metrics, and recommended next steps. Acceptance criter...

...between Administrator, Facility Manager, and Energy Analyst so each sees the right data, controls, and reports. I’m expecting you to own the architecture—data models, API design, front-end framework, CI/CD pipeline, security hardening, and automated testing—while keeping future modules and machine-learning models in mind. If you’ve shipped SaaS on Azure, tuned Fabric Lakehouses, and integrated MLOps workflows, you’ll feel at home here. Deliverables I will review for the initial milestone: 1. High-level architecture diagram with tech stack choices and reasoning 2. Clickable prototype or skeleton app that demonstrates multi-tenant login and role separation 3. Data pipeline outline showing how raw streams land in Fabric, are transformed, and beco...

Core Focus: Roughly 50% hardening and governing cloud data pipelines, and 50% building business-facing AI/ML Proof of Concepts (POCs). Support MLOps infrastructure to operationalize, monitor, and scale models built by data science teams. Key Responsibilities Pipeline & Feature Engineering: Design, automate, and scale robust data pipelines for AI/ML workloads. Build and manage feature stores to support model training and inference. Data Integration: Ingest and transform structured and unstructured data sources using AWS Glue, Snowflake, and Informatica Data Management Cloud (IDMC). MLOps & Automation: Implement DevOps/MLOps practices and CI/CD pipelines (via GitHub Actions). Monitor pipeline performance, troubleshoot production issues, and mitigate model ...

...experienced AI/ML Engineer who combines a strong foundation in traditional Machine Learning and Data Science with hands-on expertise in modern Generative AI systems. This role requires someone who can build production-ready ML solutions, develop intelligent LLM applications, design multi-agent systems, implement Retrieval-Augmented Generation (RAG), and deploy scalable AI services using modern MLOps practices. You'll work across the full AI lifecycle—from data preparation and model development to deployment, monitoring, and continuous improvement. --- ## Responsibilities * Design, build, and deploy end-to-end machine learning solutions. * Develop predictive models using classical ML algorithms for structured and unstructured data. * Build production-grade LLM appl...

...roadmap discussions, industry trend analysis, and platform improvement initiatives. ## Required Skills & Expertise * Deep expertise in Red Hat OpenShift and Kubernetes-based platforms. * Hands-on experience with workload orchestration and GPU scheduling. * Strong knowledge of Dell AI infrastructure and enterprise AI environments. * Extensive experience in AI/ML platform architecture and MLOps. * Strong understanding of secure private AI deployments and on-premise AI environments. * Experience with vulnerability management, security governance, and compliance frameworks. * Vendor management and multi-stakeholder collaboration experience. * Excellent documentation, communication, and technical leadership skills. * Ability to operate independently in a remote advisory role. ...

...to lightweight inference strategies that can live inside an existing AWS stack. Clear explanations of trade-offs, evaluation metrics, and maintenance plans are essential because executive buy-in depends on measurable ROI. If your guidance consistently translates into time savings or accuracy gains, the engagement will expand into a multi-year collaboration covering iterative model improvements, MLOps automation, and ongoing performance monitoring. Typical session deliverables: • A concise technical note or diagram summarizing the discussed solution • A prioritized action list I can hand straight to my engineering team • Pointers to relevant code samples or research papers Availability for at least one 60-minute slot per week is preferred, but I’m fle...

I want a working proof-of-concept application that pulls together a modern MLOps stack on Kubernetes. The build should spin up from a clean repo, flow through a CI/CD pipeline, and deploy an end-to-end demo that highlights three AI pillars I care about: Nvidia AI Enterprise for accelerated inference and training, production-grade LLM functionality (LangChain-powered), and Retrieval-Augmented Generation using a vector store. Core workflow • Code is pushed → pipeline (GitHub Actions or similar) runs tests, builds the container image, and promotes it to the cluster. • Helm or ArgoCD handles drift management so that the desired state remains in sync. • At runtime, the service must auto-scale both CPU and GPU requests, proving horizontal and vertical elasticity...

...like GPT, Gemini, and LLaMA. • Implement RAG, vector search, prompt orchestration, and model evaluation. • Partner with data scientists to productionize POCs. Data & Platform Engineering • Build distributed data pipelines (Python, PySpark). • Develop APIs, SDKs, and integration layers for AI-powered applications. • Optimize systems for performance and scalability across cloud/hybrid environments. MLOps / LLMOps • Contribute to CI/CD workflows for AI models—deployment, testing, monitoring. • Implement governance, guardrails, and reusable GenAI frameworks. Collaboration & Stakeholder Engagement • Work with analytics, product, and engineering teams to define and deliver AI solutions. • Participate in architecture reviews and...

...platforms such as Azure, AWS, or Google Cloud. * Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI. * Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools. * Knowledge of LLM safety, guardrails, prompt injection prevention, and responsible AI practices. * Experience with MLOps, CI/CD, monitoring, observability, and model performance tracking. ## Technical Skills * Python, SQL, APIs, FastAPI * LLMs, NLP, embeddings, RAG, AI agents * Vector databases and search systems * Prompt engineering and LLM evaluation * Fine-tuning and preference optimization * Cloud deployment and scalable AI systems * Data analysis, experimentation, and model monit...

...platforms such as Azure, AWS, or Google Cloud. * Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI. * Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools. * Knowledge of LLM safety, guardrails, prompt injection prevention, and responsible AI practices. * Experience with MLOps, CI/CD, monitoring, observability, and model performance tracking. ## Technical Skills * Python, SQL, APIs, FastAPI * LLMs, NLP, embeddings, RAG, AI agents * Vector databases and search systems * Prompt engineering and LLM evaluation * Fine-tuning and preference optimization * Cloud deployment and scalable AI systems * Data analysis, experimentation, and model monit...

...Expected start: immediately after award. - Freelancer must propose a fast delivery plan with concrete dates. - Preferred target: first working version in 2-4 days, final handover in 5-7 days (unless a justified alternative is approved). ## Skills Required - Strong Python experience - OCR post-processing and noisy text matching - Similarity algorithms (Levenshtein / weighted scoring) - Practical MLOps mindset for production inference pipelines - Experience with AWS Lambda/S3-style workflows is a plus ## Proposal Instructions for Freelancer Please include in your proposal: - Similar projects where you matched noisy OCR text to structured inventory labels - Proposed scoring/matching strategy - Testing strategy and an **ASAP** timeline with dates - **Fixed-price quote** with clear ...

...* Trained machine learning model * Model evaluation report * Feature engineering documentation * Deployment-ready package * API for prediction (if applicable) * Technical documentation * Installation and usage guide Required Experience * Data Engineering and ETL development * Machine Learning for tabular datasets * Agricultural analytics or related domains (preferred) * Model deployment and MLOps practices * Python-based data processing frameworks Proposal Requirements Please include: 1. Relevant project experience. 2. Similar ETL or machine learning projects completed. 3. Proposed technology stack. 4. Estimated timeline. 5. Project cost estimate. 6. Approach for handling data preprocessing, model training, and deployment. We are looking for a scalable, maintainable, and ...

I need an experienced AI consultant / architect to step in and re-shape the core of our SaaS platform so it runs smarter, faster, and at lower cost. The immediate focus is product funct...in. • A phased implementation roadmap with clear milestones, risks, and success metrics tied to measurable efficiency gains. • An initial proof-of-concept inside a staging environment demonstrating at least one key automation or optimisation feature we can benchmark. I will provide access to repos, data samples, and our DevOps pipeline. You bring deep knowledge of scalable ML engineering, MLOps best practices, and SaaS product thinking. If you have shipped AI features that cut compute time, auto-scale intelligently, or removed repetitive tasks for end-users, tell me about it and let&...

...our existing data assets, then designing a scalable machine-learning stack that can move from PoC to production without rewrites. I expect you to be comfortable choosing the right mix of Python, scikit-learn, PyTorch or TensorFlow, setting up automated training pipelines, and wiring everything into a cloud environment (AWS, Azure or GCP—whichever best fits the roadmap we draft together). Strong MLOps habits, version-controlled experiments and CI/CD for models are must-haves because this project must be repeatable by the Pune team after hand-off. Key deliverables • Architecture blueprint covering data ingestion, feature store, model training, serving and monitoring • Detailed project plan with phased milestones and risk mitigation steps • A working MVP...

...Channel - @bitfid) Role: Freelance YouTube Designer & Animator Channel: bitfid (@bitfid) Content Type: AI infrastructure, open-source AI, Kubernetes, MLOps, cloud, and technical explainers Engagement: Freelance / project-based / part-time Location: Remote About the Channel I run a YouTube channel focused on explaining complex AI infrastructure and open-source technology in a simple, visual, and engaging way. Topics include: Agentic AI Kubernetes for AI workloads vLLM, KServe, Kubeflow, Kueue NVIDIA AI infrastructure Graph RAG Open-source AI stacks MLOps and cloud-native AI The goal is to make technical content understandable for engineers, founders, students, and tech-curious viewers. I am looking for a creative freelancer who can help improve

...(OpenAI, Anthropic, Cohere, open-source) and outline the data, prompt-engineering, RAG, and governance strategy required for safe deployment in regulated BFSI environments. • Produce at least two working PoCs that demonstrate measurable gains in first-contact resolution, average handling time, or cost-to-serve, ready for pilot rollout with a flagship NBFC. • Define a phased talent, tooling, and MLOps plan so our product and delivery teams can scale these capabilities across the rest of the platform. • Establish success metrics and a commercial model that converts automation gains into new subscription or usage-based revenue streams. Success will be measured by a signed-off roadmap, live PoCs, and an implementation blueprint that product, engineering, and sal...

...backend services, APIs, dashboards, and customer-facing features - Support ML/MLOps workflows including model training and deployment - Implement monitoring, error handling, and automated recovery systems - Ensure data quality, validation, and anomaly detection - Design reusable, scalable data schemas and APIs - Contribute to CI/CD pipelines with automated testing and deployments Required Skills: - Strong Python (5+ years), SQL, Pandas/PySpark - AWS expertise (Lambda, Glue, S3, Athena, DynamoDB, ECS Fargate) - Experience with data lakes and serverless architectures - Backend/API development (REST, microservices, event-driven systems) - Working knowledge of Node.js or willingness to learn - Exposure to ML/MLOps (SageMaker, model lifecycle) - Experience with large-scale da...

This training program is designed to provide participants with practical knowledge of building, deploying, and managing Machine Learning solutions usi...knowledge of building, deploying, and managing Machine Learning solutions using Google Cloud Platform (GCP). The program focuses on real-time industry use cases, hands-on implementation, and cloud-based ML workflows. Participants will gain experience in: Data preparation and preprocessing on GCP Building and training ML models Using Vertex AI and other GCP ML services Model deployment and monitoring MLOps concepts and automation Real-world project implementation using cloud-based ML solutions The training aims to help professionals apply GCP-ML concepts in enterprise projects and prepare for real-time business scenarios and certi...

...platform features - Contribute to frontend dashboards (Vue.js / ) - Support ML/MLOps workflows (training, deployment, lifecycle on SageMaker) - Implement monitoring, error handling & ensure high system reliability (99.9% uptime) - Build data validation, quality checks & anomaly detection systems - Design systems for backfills, reprocessing & consistency - Maintain data contracts, schema versioning & CI/CD pipelines --- Required Skills - 3–7+ years of software/data engineering experience - Strong Python (5+ yrs), SQL, Pandas/PySpark - Hands-on AWS (Lambda, Glue, S3, Athena, DynamoDB, ECS, Step Functions) - Experience with REST APIs, microservices, event-driven architecture - Knowledge of ML/MLOps (SageMaker, model lifecycle) - Exposure to Node.js (...

...my raw data, handle preprocessing, select and justify the algorithm, train, tune, and deliver a production-ready model with accompanying Python code (TensorFlow, PyTorch, or scikit-learn are all acceptable). Your proposal should focus on your relevant experience with similar supervised learning projects—please highlight datasets you have tackled, algorithms you excel with, and any deployment or MLOps know-how. No lengthy sales pitches; I want to see evidence that you can translate business objectives into high-performing, explainable models. Deliverables • Clean, well-commented source code and notebooks • Reproducible training pipeline • Evaluation report covering accuracy and at least one additional metric appropriate to the problem (e.g., F1, ROC-AUC)...

Urgent Hire | Python RPA Engineer — NLP + Computer Vision What We're Building We're working on a time-sensitive automation project that requires a battle-tested Python RPA developer who can hit the ground running — no handholding, no ramp-up...Selenium, you must have shipped real projects with these • Computer Vision — TensorFlow, offline models only (zero external API calls) • NLP — spaCy-based pipelines, offline and self-contained • Neural Network design — hands-on architecture and training experience, not just fine-tuning wrappers • Web scraping at scale — robust, fault-tolerant implementations Good to Have • MLOps experience — model versioning, deployment, monitoring • Exp...

I am look...experience with PyTorch or TensorFlow Proven experience in computer vision (CNNs, image classification) Experience with active learning / human-in-the-loop systems Understanding of model calibration and uncertainty estimation Ability to design production-ready ML pipelines Nice to Have: Experience with medical or biological image data Familiarity with annotation tools (e.g., Label Studio or similar) MLOps / deployment experience Please share: Examples of similar projects (especially active learning or HITL systems) Your approach to implementing uncertainty-based sampling Suggested improvements you would explore for our use case We are looking for someone who can think critically about the system and help us significantly reduce manual effort while improving model pe...

...27001 / GDPR readiness) through secure-by-default engineering practices CORE SKILLS REQUIRED BACKEND Node.js, Python (FastAPI/Flask), RESTful APIs, microservices, PostgreSQL, Redis, message queues FRONTEND React, TypeScript, responsive UI, dashboard systems, data visualisation (D3 / Recharts) MACHINE LEARNING ML model deployment, fraud detection pipelines, risk scoring, scikit-learn / PyTorch, MLOps basics CLOUD & DEVOPS AWS (Lambda, RDS, S3, API Gateway), Docker, CI/CD (GitHub Actions), infrastructure as code REGTECH / COMPLIANCE AML, KYC/KYB, PEP/Sanctions, identity document verification, GDPR, FCA regulatory context SECURITY Secure API design, OAuth2/JWT, data encryption, OWASP best practices, role-based access control EXPERIENCE & QUALIFICATIONS 5+ years of profession...

I’m scheduling an advanced, hands-on course that will run from 27 April 2026 for one to two weeks. The participants—data scientists, data engineers, DevOps and MLOps engineers—already work daily with Spark and cloud pipelines; what they need is a deep dive into building production-grade Databricks AI agents. Beyond code walkthroughs, I expect you to cover the entire lifecycle: designing agentic AI workflows, wiring those agents to Databricks clusters and MCP servers, operationalising them with MLflow and Unity Catalog, and enforcing governance at scale. The emphasis throughout the program should stay on Databricks AI agents, as that is the area the team must master. To shortlist you, I’ll need the following: • Your portfolio that proves you have del...

...advanced, but I want to reinforce the foundations and then push further into Machine Learning, Deep Learning, and Data Visualization while working through real business problems. Scope • Daily live sessions (about 60–120 minutes, Monday-Friday) for 12 consecutive weeks. • A structured curriculum that begins with a quick Python + statistics refresher and moves swiftly into sophisticated modelling, MLOps, and the latest AI techniques. • Practical, code-along labs in Jupyter or VS Code after every concept—no passive slide decks. • One continuous real-time financial-modelling project (e.g., credit-risk scoring, portfolio optimisation, or time-series forecasting) plus smaller mini-assignments. • A capstone that demonstrates the full pipe...

We are looking for a skilled AI Engineer to...experience as an AI Engineer, Machine Learning Engineer, or similar role Strong knowledge of Python and libraries such as TensorFlow, PyTorch, or scikit-learn Experience with APIs and model deployment (Docker, Kubernetes, or cloud platforms like AWS/Azure/GCP) Familiarity with LLMs, NLP, or computer vision is a plus Ability to communicate clearly and work independently Nice to Have: Experience with MLOps pipelines Background in data engineering or DevOps Experience integrating AI into production systems Project Details: Type: Freelance / Contract Duration: To be discussed Budget: Open (based on experience) Start: ASAP If you’re interested, please share: Your portfolio or previous AI projects Relevant experience Availability a...

...also comfortable branching into Reinforcement Learning or NLP later, that flexibility will be a plus for the longer roadmap, but the immediate priority is Deep Learning mastery. The sessions will be delivered live (online or hybrid can be arranged), and I’ll rely on you to: • Shape a clear, week-by-week syllabus covering CNNs, RNNs, transformers, optimisation tricks, model interpretability and MLOps basics using Python, TensorFlow or PyTorch • Provide concise slide decks, hands-on notebooks (Jupyter/Colab) and at least three graded mini-projects that mirror industry use-cases • Guide learners through code reviews and Q&A, then wrap up with a capstone evaluation and feedback report All teaching material must be original or properly licensed, and rea...

...single requirement, not multiple specialist roles. The person should be strong across modern AI engineering and capable of taking problems from architecture and prototyping through optimization, deployment, and production readiness. The work may span LLMs / SLMs, recommendation engines, agentic interview workflows, AI-based result assessments, multimodal AI systems, classical ML, deep learning, and MLOps. This role is best suited for someone who is a strong AI generalist with solid engineering discipline and the ability to convert ambiguous problem statements into practical, scalable AI systems. The source role requires 5+ years of experience entirely in the AI/ML domain. What You Will Work On - Design and build AI solutions across multiple use cases and workstreams Develop a...

My website offers AI consulting plus machine-learning and data services, and I want to strengthen its search visibility through strategic backlink outreach only. I’m focused on earning do-follow links from reputable technology blogs with solid domain authority and a genuine readership interested in artificial intelligence, data science, MLOps, cloud, and related topics. What I need from you: • Research and compile a list of high-quality tech blogs that accept external contributions or link placements. • Pitch and secure contextual backlinks that read naturally inside relevant, value-adding articles or resource pages. • Provide a brief placement report for each live link, including URL, anchor text, DA/DR, and publication date. I don’t currently have a ta...

...is an initial qualifier to assess fit before we share further details. Please respond only if you are based in Jaipur, Rajasthan and can support in-person collaboration if needed. --- We'd like you to share: 1. Brief company/individual profile – who you are and how long you've been active in AI/ML 2. Core areas of expertise – e.g., NLP, computer vision, predictive modeling, data pipelines, MLOps, LLMs, etc. 3. 2–3 relevant past projects – industry, problem solved, tools/tech used (no confidential details needed) 4. Team size & structure – solo practitioner, small team, or company? 5. Availability – current bandwidth and ability to take on a new engagement 6. Hourly charges – please quote in INR (₹/hour). If you prefer a ...

...with the core orchestrator, no third part tool. • Web navigation/ scraping with Selenium/Playwright: document download, classification, OCR/text extraction. • Build/train neural networks (e.g., CNNs for image doc classification). • NLP expertise with spaCy for entity extraction. • Computer vision using TensorFlow/OpenCV (offline Vision Libraries preferred). Preferred Skills: • MLOps (e.g., MLflow, Docker for deployment). • Strong problem-solving for complex, error-prone workflows. • 2+ years portfolio with RPA/CV projects (GitHub links required). Project Details: • Milestones: Week 4 (scraper prototype), Week 8 (CV model), Week 12 (full RPA pipeline). • Tools: Python 3.10+, Git, Jupyter. Patient, met...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

I need an experienced AI/ML freelancer to take the lead on building a custom copilot that streamlines day-to-day clinical and administrative workflows for a healthcare platform. The immediate goal is to move from scattered manual steps to an intelligent...Slack/Teams bot • At least three predefined healthcare workflows automated end-to-end (e.g., visit note drafting, prior-auth request, lab follow-up) • Clear README with setup, environment variables, and model/embedding choices • Security checklist confirming HIPAA-ready data handling and access controls Once the copilot’s core loop is stable, we can expand into fine-tuning, evaluation, and full MLOps deployment, but the focus right now is that initial working assistant. I’m ready to start as soon a...

...enforcing context bound generation and preventing hallucination outside retrieved evidence. Indexing is parallelized using ProcessPoolExecutor for efficient multi core utilization and automatically scales to distributed ingestion via PySpark when corpus size exceeds a configured threshold, enabling safe handling of 20k plus documents or 50GB class corpora, while the system is wrapped in a full MLOps backbone that integrates MLflow for experiment tracking of retrieval metrics, PPO reinforcement learning rewards, and parameter tuning, exposes Prometheus metrics for latency and retrieval monitoring compatible with Grafana dashboards, and supports Airflow DAG orchestration for scheduled indexing and policy training workflows. Reinforcement learning is implemented using a PyTorch base...

Azure MLOps Trainer Needed for Training Sessions We are hiring a senior Azure AI engineer to provide structured, hands-on training in building enterprise-grade LLM systems. This is NOT a beginner AI course and NOT a chatbot project. We need practical training in implementing: - Azure OpenAI API integrations (production-ready) - Full RAG pipelines using Azure AI Search (vector + hybrid search) - Document ingestion workflows (Blob > OCR > chunking > embeddings) - Function/tool-calling for agentic workflows - Secure deployment using Azure Functions / Container Apps / AKS - Logging, retries, structured validation, and reliability patterns - Enterprise constraints (RBAC, private endpoints, managed identity) The focus is: - Clean architecture - Production patterns - Observabi...

Senior AI / ML Architect – GenAI, MLOps & Enterprise AI Work Support (10+ Years) Job Description We are seeking a highly experienced AI/ML professional (10+ years) to provide ongoing technical work support across advanced AI, GenAI, and data-driven systems. This role involves hands-on guidance, design reviews, problem-solving, and production support for complex AI/ML implementations in enterprise environments. The ideal candidate has deep real-world experience and can quickly understand requirements, identify gaps, and provide clear technical direction. Candidates may specialize in any subset of the skills listed below. Core Expertise (Any of the Below) Generative AI & LLM Systems LLM-based applications and enterprise GenAI platforms Prompt design, alignment, ...

...automatically scouts freelancing websites, general job boards, and specialised training platforms for roles or courses that involve artificial-intelligence work. The agent must: • Crawl and scrape the relevant pages in real time or on a frequent schedule. • Apply NLP or other classification techniques to decide whether a posting is truly AI-related, then tag it by sub-domain (e.g. vision, NLP, MLOps, prompt-engineering). • Deliver concise, deduplicated listings to me through an in-app notification feed—no email or SMS required. For the deployment side I’m open to Python (Scrapy, BeautifulSoup, Selenium), Node, or any stack you are comfortable with so long as it is containerised and can run unattended on a small cloud instance. A lightweight web in...

...criteria A working API must return real-time predictions within agreed latency limits, integrate seamlessly with the current SMTP/ESP workflow, and include logging for compliance review. Final delivery is considered complete when the system runs in production and all documentation passes peer review. Tools & stack Python, scikit-learn or TensorFlow, SQL/NoSQL for data storage, and standard MLOps utilities (Docker, CI/CD) are anticipated, yet alternative libraries are welcome if they meet the same reliability and security standards. Timeline and milestones will be outlined together at project start, with code reviews scheduled at each major checkpoint....

...model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding is essential; I want clear explanations of feature importance, assumptions and limitations that business stakeholders can grasp quickly. Big-data exposure, cloud familiarity (Azure, AWS or GCP), ETL pipeline design and MLOps practices are all welcome extras—you’ll have room to propose improvements if they make the solution more robust or scalable. Deliverables I need from you: • A well-documented predictive model with reproducible code and clear version control • Cleaned and transformed datasets stored back into SQL (or a recommended alternative) • An interactive Pow...

...Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent analytical and communication skills • Experience with Generative AI and LLM frameworks • Knowledge of Docker, Kubernetes • ...

...Required Qualifications 8+ years of experience as an AI Architect or similar role, with proven expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, GPT models, RAG systems). Strong background in bridging business needs to technical implementations, demonstrated through successful projects with short development cycles. Proficiency in cloud platforms (AWS, Azure, GCP) for AI deployment, MLOps, and scalable architectures. Experience in data engineering, NLP, computer vision, or predictive modeling relevant to business applications. Excellent communication skills to articulate complex AI concepts to non-technical stakeholders. Bachelor's or Master's degree in Computer Science, AI, or a related field. Preferred Qualifications Prior experience in travel, hospitalit...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

...best practices. Required Skills Strong understanding of AI/ML concepts such as: Predictive analytics, forecasting, classification, NLP/LLM, GenAI, RPA, model evaluation, etc. Ability to translate business problems into AI/ML use cases. Ability to communicate complex technical concepts to non-technical audiences. Familiarity with modern data platforms, cloud providers (AWS/Azure/GCP), and MLOps practices. Experience in proposal creation, solution pitching, and pre-sales cycles. Acceptance Criteria Minimum 5 years of experience in AI/ML solutioning, consulting, or presales. Experience leading client discussions and presenting solutions. Demonstrated ability to define business use cases and value propositions. Exposure to enterprise customers or large-scale business tran...

...while ensuring compliance, deliverability, and scalability. We are deliberately not prescribing tools. You are expected to choose the right architecture and tooling based on the requirements below. What We Need Built (Outcomes) 1. Demand Signal Detection System: Design a system that can automatically identify companies that are actively hiring for roles such as LLM Trainers, AI / ML Engineers, MLOps Engineers, Model Evaluators, AI Researchers. The system should prioritize high-intent signals, such as recent job postings, repeated hiring for similar roles, active recruiter or TA hiring activity, output should be a daily, refreshed list of qualified companies 2. Account & Contact Mapping: For each qualified company, the system should: - Identify the right decision-layer co...

...suggested timings. Packaged folder: PPTX/Google Slides, PDF handouts, images, quiz spreadsheet, rubric files. Courses to produce (high level) Data Literacy & Governance (Dummy→Hero, templates for audit, DPIA, stewardship) AI Literacy, Governance & Security (model audit, privacy for ML, incident playbooks) AI Mastery — Advanced (deep dives: evaluation, interpretability, advanced privacy, production MLOps best practices, research-to-production workflows) AI Graphics & Video Editing (practical: prompt engineering for visuals/video, workflows for AI-assisted editing, tools pipeline, export-ready assets) Required skills & experience (must have) Instructional design for adult learners (tech/enterprise audiences). Strong slide-deck design skills &mdas...

...with? How do you typically define and defend novelty and contribution in applied research? Describe your experience responding to reviewer comments. Give a sample of Similar Books you have published and publishers you worked with ======================================= The works are based on original material (slides, transcripts, frameworks) focused on AI infrastructure, data centers, energy, MLOps, and applied AI economics. additional projects in clude -AI Literacy, Governance & Security; Data Literacy & Governance This is not marketing content or SEO writing. We are looking for someone who understands peer-reviewed publishing, scholarly contribution, and editorial positioning. Scope of Work Journal Paper Shape a journal-ready manuscript aligned to Q1/Q2 journals ...

...devoted to pushing the limits of artificial intelligence, and I’m ready to bring a committed AI software engineer into the core team. You will own the software layer of our AI stack—designing training pipelines, shaping inference services, integrating state-of-the-art models, and turning ambitious ideas into production-ready code. Expect to work hands-on with Python, PyTorch or TensorFlow, modern MLOps tooling (Docker, Kubernetes, CI/CD), and a cloud platform such as AWS or GCP. Because we’re still small, your voice will matter. Whether your strongest suit is classic machine learning, NLP, computer vision, or another specialty, it’s the ability to turn theory into robust, well-tested code that counts. Deliverables • An initial proof-of-concept ser...

...passionate about solving real-world problems using AI and security-driven approaches. I enjoy working at the intersection of machine learning, cybersecurity, and data science, with a particular interest in secure machine learning systems, threat detection, and intelligent data modeling. Key Skills: Machine Learning, Cybersecurity, Cryptography, Data Analysis, Statistical Modeling, Python, Java, C, SQL, MLops (Airflow, Mlflow, Docker, FastAPI), Forensics Tools, Network Security (Wireshark), SPSS, SAS, R....