276 to 300 of 603 MLOps Jobs in England

KDR Talent Solutions Ltd seeks an experienced ML Engineer to drive end-to-end ML development, including pipeline building, model deployment, and ML Ops. You will collaborate with cross-functional teams to integrate ML into ...

Proximie Limited is hiring a machine learning engineer to own the training and deployment infrastructure for our production ML models. You will manage the training and inference workloads on AWS SageMaker, build reproducible training runs ...

Barclays Bank PLC in London seeks an AI Platform Engineer to design and operate scalable AI infrastructure, optimising machine learning workloads and supporting trading decisions within Global Markets. You will work with Python, Kubernetes, GPU ...

Xelix, a fast-growing fintech‐adjacent scale‐up in London, is hiring a Senior DevOps Engineer to own reliability, scalability and security of our cloud platform. Based in London, you’ll work from the office ...

APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses required for production use. Cloud, DevOps & MLOps Execution: Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices to ensure applications are scalable, resilient, cost-aware, and supportable. Knowledge ...

APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses required for production use. Cloud, DevOps & MLOps Execution: Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices to ensure applications are scalable, resilient, cost-aware, and supportable. Knowledge ...

generation (RAG) architectures and agent frameworks such as Google ADK and LangChain, helping transform how the Bank serves customers and empowers colleagues. Data engineering, MLOps and intelligent automation , creating resilient data pipelines, scalable deployment architectures and automated machine learning workflows using contemporary engineering and CI/CD practices, enabling … machine learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering,customisationand integration Generative and Agentic AI MLOps& cloud computing It’sall live lectures, tutor-led groupworkand real-world challenges from day one. Learn it. Apply it. Keep going Your personal learning plan ...

Head of Engineering and Technical Architect Cloud & Platform Integration: Partner closely with our GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines, ensuring scalable and cost‐effective model deployment via Vertex AI and containerized environments. Take ownership of building and maintaining robust LLMOps pipelines. … data warehouse architectures. Hands‐on experience with Google Cloud Platform (GCP) services including Vertex AI, BigQuery, Cloud SQL, and Google Cloud Storage (GCS). MLOps/LLMOps Pipelines: Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/ ...

understand model behaviour before and after deployment. End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration. MLOps/LLMOps: CI/CD for models, containerisation/orchestration, experiment tracking and registry, model evaluation pipelines, safety guardrails, canaries, and performance monitoring. Cross-team … learning, computer science, engineering, mathematics or a related technical field. Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. Desirable MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. Data engineering: proficiency with Databricks, Apache Spark, Delta Lake, MLflow, and SQL; experience ...

Head of Engineering and Technical Architect. Cloud & Platform Integration: Partner closely with our GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines, ensuring scalable and cost‐effective model deployment via Vertex AI and containerized environments. Take ownership of building and maintaining robust LLMOps pipelines. … data warehouse architectures. Hands‐on experience with Google Cloud Platform (GCP) services including Vertex AI, BigQuery, Cloud SQL, and Google Cloud Storage (GCS). MLOps/LLMOps Pipelines: Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/ ...

Generative AI solutions, including RAG architectures, prompt engineering, and integration with Azure AI services (e.g., Azure OpenAI, Azure AI Search). Ensure robust MLOps practices are followed, including model versioning, testing, deployment pipelines, and monitoring of deployed solutions for performance and drift. Champion engineering best practices across the team, including … data processing. Familiarity with Generative AI concepts and tooling, including working with LLMs, prompt engineering, RAG patterns, and vector search. A solid understanding of MLOps principles, including CI/CD for models, automated testing, and monitoring of deployed solutions. Cloud platform experience, preferably Azure, including services such as Azure OpenAI ...

London, United Kingdom | Posted on 16/10/2026 Role: MLOps Engineer- Contract Location: London |Hybrid- 3/4 days in client office. Industry: Financial Services/Banking Start Date: Nov 2026 Agreed Contract Rate (ACR): £308.33 per day Payroll provider:RockfordPayroll Info for Contingent Workers – Rockford Pay Daily … Daily Rate: Up to £400 per day, dependenton experience Equivalent Annual Salary: £71,531.56 + £8,633.12holiday pay The Role Weare looking for experienced MLOps Engineers to join a leadingglobal financial services organisation, working on cutting-edge Generative AI,machine learning and intelligent automation solutions. This is an excitingopportunity ...

part of building the bank of the future. Job Summary As a Software Engineer III at JPMorgan Chase within the International Consumer Bank MLOps team, you will design, build, and deploy scalable AI/ML solutions that promote innovation and enhance the banking experience. You will collaborate with data scientists … technologies, distributed systems, RESTful APIs, and web technologies Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Hands-on experience with cloud computing platforms like AWS, Azure, or GCP Experience working ...

realize your potential. Our team is key to our success. We’re people-first. We value collaboration, curiosity and commitment. As a Lead MLOps Platform Engineer at JPMorgan Chase within the Accelerator, you are the heart of this venture, focused on getting smart ideas into the hands of our customers. … quality maintainable solutions Required qualifications, capabilities and skills Proficiency in coding in recent versions of Java and/or Python programming languages Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) Experience with cloud technologies (AWS/Azure/GCP) and distributed systems ...

mobility while building the bank of the future. Job Summary As a Lead Software Engineer at JPMorgan Chase within the International Consumer Bank MLOps team, you will design, build, and deploy scalable AI/ML solutions that promote innovation and deliver outstanding banking experiences. You will collaborate with data scientists … prioritize deliverables across multiple work streams Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to productionising machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Proficiency in cloud-native microservices architecture Hands-on experience with Amazon Web Services (AWS) Previous experience ...

design, build, and operate the infrastructure that enterprise AI and Generative AI workloads run on: the platform layer beneath LLMs, agents, and MLOps pipelines. This spans GPU-accelerated compute and container platforms, model serving and gateway infrastructure, evaluation and guardrail systems, and the MLOps/LLMOps tooling that takes … operate core AI platform components: model serving and gateway infrastructure, agent orchestration and tool-calling frameworks, evaluation harnesses, and guardrail/governance layers• Implement MLOps and LLMOps pipelines (model deployment, monitoring, retraining, and fine-tuning where relevant) using Infrastructure-as-Code, GitOps, and CI/CD• Establish observability, security ...

/ML and Generative AI solutions. You will own the architecture, governance, and technical direction across LLMs, agentic systems, NLP, RAG architectures, and MLOps on Google Cloud Platform (GCP). The role requires hands-on expertise, stakeholder management, and delivering secure, scalable AI platforms that drive measurable business outcomes. #J ...

systems within the Markets Research Technology team, driving AI-assisted practices and scalable microservices. You will guide development across data engineering, cloud infra, and MLOps, mentoring teams and shaping robust software solutions for the firm’s business objectives. #J-18808-Ljbffr ...

demands a Master's or PhD in a quantitative field, requiring strong skills in statistics and machine learning, including extensive experience with Pytorch and MLOps tooling. You will build scalable Data Science capabilities and collaborate with engineers to deploy Machine Learning services. This opportunity blends research and engineering, ideal ...

involves designing, deploying, and optimising machine learning models and AI workflows within Azure. The ideal candidate will have experience with Azure Databricks, LangChain, and MLOps practices. The role offers an attractive salary of up to £82,000, an annual performance bonus, and a comprehensive benefits package including private medical insurance ...

products used by millions of customers every day. This is a highly collaborative role where you’ll partner with Product Managers, Engineers and MLOps teams to take ideas from experimentation into production. You’ll also help shape technical best practice across the team, championing robust experimentation, high-quality code ...

frameworks (TensorFlow, PyTorch, scikit-learn). Comfortable with time series data and forecasting problems. Experience with cloud platforms (AWS, GCP, or Azure) and MLOps practices. Curious about energy, climate, or sustainability — or excited to learn. A pragmatic builder who cares about shipping things that work, not just impressive prototypes #J ...

Required: Demonstratable years' experience in a Data Scientist position Strong Python development skills with experience building and deploying machine learning solutions Experience working with MLOps frameworks and best practices Strong understanding of CI/CD principles and deployment processes Experience developing, tuning, testing, and optimising machine learning models Good understanding ...

optimisation The ability to debug and improve model performance systematically Also Valued Knowledge of distributed training or large‐scale data processing Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning) A background in applied research or publishing Familiarity with retrieval systems, embeddings or ranking models Ideally ...

optimisation The ability to debug and improve model performance systematically Also Valued Knowledge of distributed training or large-scale data processing Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning) A background in applied research or publishing Familiarity with retrieval systems, embeddings or ranking models Ideally ...