26 to 50 of 305 MLOps Jobs in England

Azure) and containerisation (Docker, Kubernetes). Solid understanding of ML fundamentals (model training, evaluation, common architectures). Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar). Building data pipelines (real‐time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt. Familiarity with ...

operational efficiency. You will be expected to operate across the full ML lifecycle, from modelling and experimentation through deployment, monitoring and continuous improvement. Strong MLOps discipline is essential, including CI/CD, automated testing, repeatable deployments, versioning, and safe release practices to keep models stable, observable and maintainable in production. ...

such as GraphDB, Stardog, Neo4j, Amazon Neptune, or Blazegraph. Experience with embeddings, vector databases, RAG, LLM-based enrichment, or knowledge graph completion. Experience with MLOps tools such as Docker, Kubernetes, MLflow, Weights & Biases, Airflow, or similar. Experience with AWS, GCP, or Azure. Experience in construction, architecture, BIM, building materials, technical ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

Multimodal Pipelines – Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services. Advanced AI/ML Frameworks – Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms like Google Vertex AI or AWS Sagemaker. Education – BS in Computer Science, Engineering, Data Science, or equivalent technical field. Unleash ...

model artifacts, and inference outputs. Maintain audit trails for model decisions, data access, and deployment history. Attributes Required Qualifications 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role. Strong proficiency in Python for ML pipeline development, tooling, and automation. Hands-on experience with ...

monitoring, logging and observability platforms • Strong troubleshooting and communication skills • Experience working with Kubernetes, CI/CD pipelines or distributed systems • Exposure to AI, MLOps or LLM-based systems would be beneficial Nice to have: • Experience with RAG pipelines or orchestration frameworks • Exposure to Databricks, MLflow or AI observability tooling ...

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

EXPERIENCE Strong experience delivering data science or machine learning solutions in production environments. Proven capability deploying and scaling models using appropriate engineering and MLOps practices. Advanced Python and SQL skills, with experience working in cloud environments such as AWS, GCP or Azure. Familiarity with modern tooling such as Databricks, version ...

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

engineering initiatives Mentor and support junior engineers within the team Contribute to engineering standards, reusable patterns, and AI best practices Help shape scalable MLOps and AI deployment approaches Drive high-quality engineering practices across testing, deployment, monitoring, and optimisation Tech environment Key technologies include: Python Azure Databricks Generative AI/ ...

domains. Ability to lead architectural discussions and technical trade‐offs without losing sight of the details. Familiarity with cloud infrastructure (AWS preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications ...

Experience managing AI systems in production environments or high scale data and ML platforms/li , li (Desirable) Experience in working with teams in MLOps, DevOps, or large scale compute environments (e.g., GPU clusters, cloud orchestration, Kubernetes)/li , li (Desirable) Experience with Generative AI evaluation, agent testing ...

analytical and conceptual skills, strong customer focus and mental agility with a results orientation • Sound understanding of GenAI solution constructs e.g., LLMs, RAG, Guardrails, MLOps and multi-modality • Experience managing and executing data science and AI projects, from ideation to deployment, while ensuring alignment with business objectives and delivering impactful ...

Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns for tools, memory, guardrails, evaluation, and observability Define MLOps protocols for agentic systems – environment separation, versioning of prompts, tools, and policies, cost controls, rate-limiting, and fail-safe mechanisms Bridge data science and platform ...

recommendation systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Publications or applied research contributions in NLP, IR, search, or generative AI Experience building AI systems in regulated, high-trust, or content-rich domains ...

support our growth. Required Skills & Experience Significant industry experience in (scientific) software development Demonstrable experience deploying AWS infrastructure as code Experience with DevOps and MLOps Degree‐level qualification in computer science or related field Professional experience with collaborative software development Deep understanding of Python Familiarity with Linux, bash ...

Learning Applied machine learning, computer vision, object detection, image classification, model development and evaluation, data annotation and curation, training pipelines, performance optimisation, edge ML, MLOps, deployment and maintenance of ML solutions, Python development, Python-based ML frameworks, PyTorch, TensorFlow, sklearn, OpenCV, NumPy, feature development, code review, pytest, linting, bug fixing ...

Scikit-learn, PySpark) Experience with LLM systems (RAG, prompt engineering, orchestration frameworks) Knowledge graphs (Neo4j or similar) End-to-end ML life cycle experience (MLOps, CI/CD, MLFlow, Docker, etc.) Consulting & Commercial Background in consulting or client-facing delivery roles Experience handling stakeholders and translating complex requirements Exposure ...

PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale ...

particularly interested in candidates with experience delivering projects within: Defence National Security Public Sector Secure Government environments Additional experience in AI Safety, Explainable AI, MLOps, AWS, Azure or GCP would be highly beneficial. Why Join? This is an opportunity to join a business at a pivotal stage of growth, where ...

analytics alongside AI solutions). Technical Scope Platform-agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing ...

GenAI solutions, from data preparation through to production deployment. Designing and building robust ML pipelines, CI/CD processes and MLOps frameworks. Leading GenAI initiatives such as document processing, LLM fine-tuning, prompt engineering and orchestration using modern frameworks. Organising and processing large, complex datasets to enable ...

vector databases AI agents and multi-agent orchestration Python, PyTorch, TensorFlow, Scikit-learn Cloud AI platforms such as AWS, Azure, or GCP Production deployment, MLOps, and scalable AI infrastructure API integration and workflow automation Bonus Points For MCP/Model Context Protocol experience Fine-tuning and evaluation frameworks Recommendation systems ...

hardware or science‐focused teams. Exceptional communication and leadership skills. Nice‐to‐Have Prior experience in startups or multi‐disciplinary product environments. Familiarity with MLOps and embedded systems integration. Background or interest in scientific/biological domains. Our Tech Stack (Current & Planned) Frontend: React, TypeScript Backend: Node.js, Python, REST & GraphQL ...