6 of 6 Permanent MLOps Jobs in West London

Intelligence, Data Science, or a related technology discipline Minimum 2:1 degree classification (or equivalent) Desirable Azure OpenAI, AWS Bedrock and Google Gemini experience MLOps, Data Engineering or Platform Engineering experience Experience leading technical teams Knowledge of Agentic AI, AI Agents and emerging AI frameworks What's On Offer? Opportunity ...

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 ...

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 ...

analytics Delivering production agentic systems, including RAG pipelines, vector search, and agentic frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and n8n DevSecOps & DevOps MLOps Infrastructure as Code Building a truly diverse, equitable, and inclusive workplace is an ongoing journey, and were excited to keep learning and growing together. We have ...

object localisation or multi-modal perception systems. GPU optimisation, CUDA, inference acceleration or edge AI. Real-time computer vision systems. Docker, Kubernetes, Kubeflow or MLOps pipelines. Aerial, satellite or ISR imagery. Synthetic data generation, Unreal Engine or NVIDIA Omniverse. Distributed training and large-scale model optimisation. Defence, government or national ...

Strategy. Design end-to-end AI solutions: Design AI solutions spanning generative AI and large language models, machine learning, and the data pipelines and MLOps needed to deploy and operate them responsibly and at scale. Set guardrails for AI agents: Define reference architectures and guardrails for AI agents, including tool … learning concepts, including generative AI and large language models, and how they are applied in enterprise solutions Understanding of the data architecture, pipelines and MLOps needed to build, deploy and operate AI solutions, and of AI and ML platforms, tooling and services (cloud AI/ML services, LLM frameworks ...