Senior AI Engineer - Agentic AI

Experteer Overview In this role you design and build scalable agentic AI platforms in a production environment. You'll work on integrating LLMs, multi-agent orchestration and RAG patterns to deliver enterprise-grade AI solutions. You'll shape the orchestration backbone, governance and observability to support secure, scalable AI workflows. This position offers impact by advancing real-world AI systems and collaborating with cross-functional teams in a hybrid London setting. Pay / Benefits

• Design, build and deploy Generative AI and Agentic AI solutions from prototype to production Implement multi-agent orchestration patterns using LangGraph, CrewAI, AutoGen, Semantic Kernel or OpenAI Agents SDK Develop orchestration backbone for advanced workflows including planning, checkpointing, retries, fallback handling and resumption of long-running processes Build and optimize RAG pipelines with embeddings, vector/hybrid search and grounded responses with citations Develop memory and context management solutions including short-term and long-term stores and compaction strategies Write robust Python APIs and services (e.g., FastAPI) with async execution, background jobs and containerized deployments Integrate enterprise systems using MCP, A2A, OpenAPI, REST and gRPC with graceful degradation and retries Apply enterprise security and governance practices including RBAC, prompt safety checks, traceability and secrets management Implement evaluation pipelines and observability frameworks using Langfuse, Arize or OpenTelemetry Contribute to architectural design decisions, code reviews and engineering standards for platform development Tasks Bachelor's or Master's degree in Computer Science, Engineering or related field (PhD is a plus) Practical experience delivering Generative AI or Agentic AI systems into production Expertise in Python engineering for APIs, microservices, testing and CI/CD workflows Strong working knowledge of LLM capabilities including prompt design, structured outputs, tool calling and retrieval strategies Hands-on experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI or Semantic Kernel) Proven experience with RAG implementations, embeddings and vector database integrations Familiarity with stateful or long-running systems including checkpointing and resumable workflows Cloud deployment experience (Azure preferred) using services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Key requirements hybrid work model London, UK location
  
  context management solutions including short-term and long-term stores and compaction strategies Write robust Python APIs and services (e.g., FastAPI) with async execution, background jobs and containerized deployments Integrate enterprise systems using MCP, A2A, OpenAPI, REST and gRPC with graceful degradation and retries Apply enterprise security and governance practices including RBAC, prompt safety checks, traceability and secrets management Implement evaluation pipelines and observability frameworks using Langfuse, Arize or OpenTelemetry Contribute to architectural design decisions, code reviews and engineering standards for platform development Tasks Bachelor's or Master's degree in Computer Science, Engineering or related field (PhD is a plus) Practical experience delivering Generative AI or Agentic AI systems into production Expertise in Python engineering for APIs, microservices, testing and CI/CD workflows Strong working knowledge of LLM capabilities including a
MLOps design, structured outputs, tool calling and retrieval strategies
• Hands-on experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI or Semantic Kernel) Proven experience with RAG implementations, embeddings and vector database integrations Familiarity with stateful or long-running systems including checkpointing and resumable workflows Cloud deployment experience (Azure preferred) using services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Key requirements hybrid work model London, UK location