1 to 25 of 44 MLOps Jobs in Central London

Machine Learning Engineer, ML Engineer, Full Stack Engineer, Full Stack Developer, Python Developer, Senior Consultant Data Science, Data Scientist, Solutions Architect AI, LLM Engineer, MLOps Engineer, AI Software Engineer, Django Developer, FastAPI Developer, React Developer, Next.js Developer, TypeScript Developer, Azure OpenAI, Vertex AI, LangChain, LangGraph, CrewAI, Autogen, OpenAI Agents ...

LangGraph, CrewAI, AutoGen); mentoring specialists without formal authority; AI governance (security, privacy, observability) Desirable: cloud platforms (AWS/Azure/GCP); Docker/Kubernetes; MLOps/DataOps monitoring and testing Personal Attributes: transversal mindset, proactive problem-solver or systems thinker, continuous learner, adaptable and pragmatic Interested? Whether your strength ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

graph databases (e.g. Postgres, Neo4j) to model and serve data behind AI applications Work closely with platform engineering throughout deployment - hosting, scaling, and MLOps/LLMOps - without owning that infrastructure yourself Define AI governance and responsible-use guardrails: data privacy boundaries, LLM governance, and policy enforcement (OPA/Rego) Instrument ...

native data services and event-driven architectures Experience building data products that support analytics, reporting, machine learning, or AI use cases Knowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patterns Experience leading distributed engineering teams Experience establishing engineering standards, reusable frameworks, and community-of-practice initiatives Knowledge ...

high‐impact team managing enterprise‐scale Azure solutions. Work on the intersection of data, AI, DevOps, and automation. Opportunities to grow across data engineering, MLOps, and cloud automation. A dynamic, learning‐focused work environment with cutting‐edge tools and processes. #J-18808-Ljbffr ...

infrastructure Transform innovative data science prototypes into robust, scalable, and secure production software, taking ownership of the 'path to production' Drive the adoption of MLOps best practices (CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists Effectively communicate the complexities ...

data pipelines and ML systems based on established patterns. Contribute to platform capabilities that support data transformation (DBT), pipeline orchestration (Airflow/Astronomer), MLOps workflows including model versioning and deployment automation and much more Support platform users including data engineers, ML engineers, data scientists, and analysts by troubleshooting issues, providing ...

making pragmatic architectural trade-offs. Strong cloud experience, ideally within Azure. Experience deploying, monitoring and maintaining ML or LLM models in production. Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management. Hands-on experience orchestrating LLM workflows using frameworks such as LangChain. An interest in how agentic software development ...

architectures Practical experience with observability stacks (Grafana, Prometheus, OpenTelemetry) Experience with generative AI platforms: LLM hosting, LLM gateways (e.g.LiteLLM, Portkey, Kong AI Gateway), and MLOps/LLMOps practices for deploying and monitoring AI systems in production Strong stakeholder communication, translating architecture into business outcomes for senior stakeholders Preferred Skills ...

Familiarity with Snowflake, Databricks, Azure AI Foundry , AWS SageMaker, and other leading platforms. Data Science Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural networks, and recommendation systems. Frameworks: TensorFlow, PyTorch, Hugging Face, OpenAI tools. Certifications: Cloud or AI architecture certifications ...

OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training ...

aligned to client objectives. Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices. Lead the day-to-day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members. Communicate analytical findings ...

working within diverse teams. A passion for continuous learning and personal development, with a willingness to explore new ideas and approaches. Familiarity with MLOps (Machine Learning Operations) practices, including the deployment, monitoring, and maintenance of AI/ML models in production environments and experience with cloud platforms and deploying ...

cross‐functional work collaborating with other teams. Strong communicator – able to explain complex technical concepts to different audiences. Experience in one or more of: MLOps for Kubernetes‐based clusters Building production systems with large language models Efficient computing based on low‐precision arithmetic. Experience writing C++/Triton/CUDA ...

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 ML workflows using contemporary engineering and CI/CD practices, enabling AI solutions … learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering, customisation and integration Generative and Agentic AI MLOps & cloud computing It’s all live lectures, tutor-led group work and real-world challenges from day one. Learn it. Apply it. Keep going Your ...

governance Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML End‐to‐end custom SageMaker pipelines for recommendation systems Design and implement the engineering components of GAR+RAG systems … problems into cutting edge data science solutions Collaborate and interface with Operations Engineers who deploy and run production infrastructure Requirements Extensive experience ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production Strong Python, Java, and/or Scala engineering Experience with statistical analysis, machine learning theory ...

Platform Engineer to design and operate an Azure-based data and AI platform. The role covers Azure landing zones, network architecture, Databricks governance, and MLOps pipelines. You will implement secure, scalable infrastructure and CI/CD processes for global teams. The candidate will work hands-on with Terraform, Bicep, Azure ...

architectures Qualifications 7+ years in enterprise architecture or AI/ML engineering Experience with cloud environments (AWS, Azure, GCP) Knowledge of AI governance and MLOps tools Work Environment A truly hybrid and flexible working environment, with the chance to shape how AI-driven advisory is delivered — working on cutting-edge ...

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 ...

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 ...

Just Eat Takeaway.com is seeking a Senior Machine Learning Engineer in a hybrid role based in Bristol/London. You will join a cross-functional team to grow product algorithmic recommendations, evolving ML capabilities across ...

MLOps Engineer (Platform/DevOps) - AI Platform - 3 days per week onsite We're partnering with a leading organisation on a high-profile AI initiative, seeking an experienced MLOps/Platform Engineer to build and scale infrastructure supporting advanced AI solutions. The Role You'll play a key role ...

3004 Avanade UK Limited Company is seeking an Azure Data Support Engineer based in London or Newcastle. The successful candidate will support Azure data solutions and manage CI/CD pipelines, ensuring robust performance. This ...

Productionise PoC/PoV work into hardened solutions with clear non-functional requirements (performance, resilience, cost, security) and defined service ownership Build and maintain MLOps/LLMOps pipelines (CI/CD, automated testing, packaging, promotion/rollback, model/version management) to enable repeatable releases Develop reusable engineering assets (libraries … experience with AI deployment patterns and infrastructure (e.g. containerisation with Docker, orchestration such as Kubernetes, API-based serving, batch/stream inference) Practical MLOps experience: CI/CD for ML, model packaging and release management, automated validation, monitoring, and lifecycle management Working knowledge of ML/DL frameworks and tooling ...