26 to 50 of 409 MLOps Jobs in London

optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) Specialism or well-researched interest in NLP Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc. Experience monitoring, maintaining, enhancing existing models over an extended time period Extensive experience with pytorch and related data science ...

work effectively in a collaborative team environment with strong problem‐solving and execution skills Experience working with Agile development methodologies Familiarity with DevOps and MLOps practices, including CI/CD pipelines, infrastructure automation, and deployment workflows Exposure to vector databases, embeddings, or orchestration frameworks used in modern AI applications Strong ...

across OCI, Data Management, AI Services or Autonomous Database. Experience working with complementary technologies such as Python, Spark, Databricks, Azure, AWS, OpenShift, Kubernetes or MLOps platforms. Demonstrated ability to structure client problems, shape solutions and identify opportunities for further value creation. Ability to travel as required to support client engagements. ...

evaluation engineering, and LLM guardrails/safety. Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring). Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations. Experience ...

experience working across engineering, data science, security, architecture, and product teams. Bonus points Experience with AI or machine learning platforms, model‐serving infrastructure, MLOps, or data‐intensive services. Knowledge of advanced networking, API management, distributed systems, service mesh, or zero‐trust architecture. Experience with GitOps, policy as code, internal developer ...

models into Azure enterprise environments. Azure Databricks, Azure Machine Learning and ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool‐calling patterns. Building end-to-end AI/ ...

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, and model governance. Excellent communication and stakeholder management skills with the ability to translate data insights into business value. Bonus Points ...

system interactions Culture and Innovation Contribute to a culture of engineering excellence, collaboration, and continuous learning Stay curious about emerging tools and approaches in MLOps and applied AI, and bring ideas for improvement to the team Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems What ...

Computer Vision models, Speech models, Vision‐Language models, and other modalities. Experience with AI model optimization, quantization, or deployment to edge devices. Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks (e.g., Kubeflow, Flyte, MLflow); proficiency with Docker and Kubernetes for AI workload containerization. Hands ...

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

across financial services, healthcare, manufacturing, retail, public sector, or other enterprise industries. Cloud & Infrastructure: Hands‐on experience with cloud architecture, Infrastructure‐as‐Code, DevOps, MLOps, or enterprise platform engineering. Pre‐sales & Customer Success: Experience supporting enterprise sales cycles, solution demonstrations, technical workshops, or proof‐of‐concept engagements. Enterprise Integrations: Experience ...

Data Mesh or Lakehouse architectures. Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis). Exposure to ML engineering pipelines or MLOps frameworks. The Zego ways of working Teams work better with time to collaborate and space to get things done. We call it Zego Hybrid: some ...

responsible AI principlesFamiliarity with cloud platforms including Azure AWS or GCP.Desirable (Not Mandatory):Experience implementing AI testing frameworks and governance controlsKnowledge of MLOps and model lifecycle managementUnderstanding of AI risk management and regulatory compliance frameworksCertifications in AI Cloud Quality Engineering or Test AutomationWe are a Disability Confident Employer:Capgemini ...

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 ...

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 ...

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 ...

Experience with Generative AI, LLMs, RAG architectures or agent‐based AI solutions. SQL and large‐scale data analysis. Cloud‐based data science platforms and MLOps practices. Data visualisation and storytelling tools. Desirable Skills Experience within financial services, regulated environments or market infrastructure. Familiarity with Azure AI services and Microsoft ...

tool use/function calling, RAG, agents, prompt engineering, or agentic patterns for enterprise workflows. Production ML/LLMOps: Experience with model deployment, monitoring, MLOps/LLMOps, and lifecycle management across cloud services (AWS Bedrock, Azure AI, GCP Vertex AI). AI Architecture & Security: Understanding of cloud reference architectures, identity ...

Microsoft Purview or comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with MLOps, model monitoring, experimental design, causal inference, forecasting, recommendation systems or optimisation. Experience with legal, financial, client, market or professional-services data, especially licensed external data ...

Nice to Have Experience with multimodal reasoning Experience with edge or offline AI deployments Familiarity with Kubernetes (EKS/OpenShift) for managing deployed applications MLOps experience: model evaluation, monitoring, reproducibility Observability tooling for agentic systems (model drift, agent behaviour, performance monitoring) Experience with agent orchestration patterns and inter‐agent communication ...

Government or Public Sector environments (essential) Eligible to obtain and maintain BPSS clearance Desirable Experience Experience delivering AI-enabled mobile applications Knowledge of MLOps, DevSecOps, CI/CD, and Infrastructure as Code Experience with vector databases, Retrieval-Augmented Generation (RAG), and AI agent frameworks Exposure to GDS standards and government ...

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 ...

London office once per week. Desirable Experience AI orchestration and workflow frameworks. Knowledge graph or semantic search technologies. Model fine-tuning and evaluation. MLOps, CI/CD, containerisation, and cloud-native deployment practices. Experience developing autonomous agents and reasoning systems. Why Join? Join a high-calibre team building a genuinely ...

technical and business stakeholders. Desirable Experience with Unity Catalog or similar data governance frameworks. Experience with Terraform or Databricks Asset Bundles. Exposure to MLflow, MLOps or AI and Generative AI workloads. Experience with streaming technologies such as Kafka or Structured Streaming. Databricks certification. Previous consulting or client-facing delivery experience. ...