476 to 500 of 639 MLOps Jobs in the UK

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: ·Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns·Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies·Understanding of MLOps, LLMOps and model lifecycle management principles·Experience designing APIs and integrating AI solutions into enterprise environments·Strong understanding of modern data architectures and platform design ...

applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting‐edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production-level … workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying ...

where appropriate to accelerate insight and delivery. Provide thought leadership and best practices across the data science lifecycle, from exploration to deployment, including scalable MLOps, model governance, and responsible AI implementation. Mentor and develop data professionals, fostering a culture of innovation, continuous learning. What We're Looking ForProven experience leading … core ML libraries (e.g., spaCy, NumPy, SciPy, Transformers).Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP).Deep expertise in MLOps principles, scalable model deployment, model governance, and the practical application of responsible AI within enterprise environments. Excellent communication and stakeholder management skills with the ability ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to‐end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object‐oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real‐time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

security risk assessments and integrate findings into governance processes.3. Secure AI/ML LifecycleIntegrate security into ML pipelines (MLSecOps) including CI/CD and MLOps frameworks. Define controls for: Secure dataset handling and lineageModel versioning and integrity validationAccess control and secrets managementEmbed automated security testing into model development pipelines.4. Data … Computer Science, Cybersecurity, AI/ML, or related field. Preferred QualificationsCertifications such as: CISSP, CISM, CCSPCertified AI Security (e.g., CAISP or similar)Experience with: MLOps platforms (e.g., MLflow, Kubeflow)AI red teaming and adversarial testingKnowledge of secure coding and DevSecOps practices. Familiarity with Responsible AI principles and ethical AI frameworks. ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

managing token, inference, and infrastructure spend so solutions stay cost effective as they scale. Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment. Apply responsible-AI judgment proportionate to the risk of each use case, working with risk … example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems. Experience implementing responsible ...

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state ...

Title: MLOps Platform Developer Location: London Salary: Depending on experience Job Type: Full time, Permanent This is a rare opportunity to be first in the door to continue the development of our engineering platform. You will own a production system end to end the web application, the database estate … with the experience and relevant job titles of: Lead Full-Stack Engineer, AI Application Engineer, Applied AI Engineer, LLM Ops Specialist, AI Platform Engineer, MLOPS Engineer, Founding Engineer, Full-Stack AI Developer, AI Software Developer may also be considered for this role. ...

that actually make it into production. We're supporting a major financial services organisation as they build a brand-new enterprise Data Platform and MLOps capability click apply for full job details ...

workflows for researchers and ML teams. You will collaborate with researchers, ML engineers and platform teams, implement IaC, ensure security and governance, and support MLOps pipelines from experimentation to deployment in a hybrid office setting (3 days in the office). #J-18808-Ljbffr ...

Banking, Financial Services and Insurance sectors, bridging C-suite strategy with engineering delivery and guiding multi-million‐pound programmes. You will oversee data architecture, MLOps, data governance, cloud migrations, and global delivery pods of 100+ engineers, delivering measurable business value while maintaining #J-18808-Ljbffr ...

collaborate across teams to enhance CI/CD, observability, and security. You’ll mentor colleagues, influence cloud patterns, and partner with Security, MLOps, and Product Engineering to deliver scalable, compliant systems. #J-18808-Ljbffr ...

involves working collaboratively to define the company's 2026 technical roadmap while solving complex challenges in autonomous systems. The role requires expertise in AI, MLOps, Software Engineering, and Data Science. The company offers a range of benefits, including family-first relocation assistance and full UK visa sponsorship. #J-18808-Ljbffr ...

mentoring engineers, delivering MVPs to production, and shaping the AI strategy across the business. You'll work with GCP Vertex AI, LangGraph, LangChain, and MLOps pipelines to ensure scalable, secure, and cost-effective solutions. #J-18808-Ljbffr ...

InfraPlat team, building and operating the infrastructure platform that powers our massive product catalog. You’ll drive multi-account/region Kubernetes clusters, MLOps, standardized deployments and the company-wide authentication platform. Collaborating with product and engineering, you’ll scope requests, identify shared pain points, mentor teammates, and help engineers ...

senior technical voice for the most sensitive AI decisions our clients face. Your work will define standards for responsible AI, model governance, and production MLOps, architect high-risk systems, and build reusable capabilities that enable multiple teams to ship safer AI at scale. #J-18808-Ljbffr ...

ensuring governance, safety, and human-in-the-loop decision-support for MoD stakeholders and prime contractors. Responsibilities include edge optimization, graph processing, AI governance, MLOps in low-bandwidth #J-18808-Ljbffr ...

Reliably fitting models with complex job dependency graphs Robustness in production, including validation and monitoring Qualifications An appreciation of good architecture and MLOPs best practice The ability to collaborate with, and influence, technical and non‐technical people A passion for end‐to‐end ownership of solutions, from articulation to delivery ...

Experience applying AI both to front-office decision-making and to how change and operations are delivered. Strong grasp of the modern AI ecosystem, MLOps, evaluation and monitoring, and responsible-AI practice. AI governance experience and the ability to make data fit for AI, working through certified data products ...

leaders. You are aligned and accountable. You are comfortable with ambiguity, fast cadence, and ownership. Excuses do not ship parts. Execution does. You have MLOps and production deployment experience at scale. Great plus: Background in composites, advanced manufacturing, aerospace, automotive, or defense. Experience with computer vision applied to automated inspection ...

fine‐tuning foundation models (LoRA/QLoRA), and deploying multi‐agent autonomous systems that have run in production not just proofs of concept. Cloud & MLOps fluency: AWS/GCP/Azure, containerization, serverless architectures, and MLOps/LLMOps tooling (Weights & Biases, LangSmith, MLflow). A product-first mindset: Fluency with ...

fine‐tuning foundation models (LoRA/QLoRA), and deploying multi‐agent autonomous systems that have run in production not just proofs of concept. Cloud & MLOps fluency: AWS/GCP/Azure, containerization, serverless architectures, and MLOps/LLMOps tooling (Weights & Biases, LangSmith, MLflow). A product‐first mindset: Fluency with ...

solutions from concept to production Develop applications using LLMs, Generative AI and Agentic AI frameworks Deliver predictive, optimisation and automation use cases Build robust MLOps and LLMOps processes, including monitoring and evaluation Work closely with engineers, data scientists, product teams and business stakeholders Help establish AI engineering standards, governance … software engineering background Commercial experience delivering machine learning models into production Hands-on experience with GenAI, LLMs and AI application development Knowledge of MLOps, LLMOps, CI/CD and containerisation Experience with cloud platforms, ideally Azure Understanding of model evaluation, monitoring and responsible AI Strong communication skills with the ability ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...