Generative AI Engineer

Role: Generative AI Engineer Location: London, UK Work mode: Hybrid - 3 days weekly from office Type: Fulltime-Permanent Experience: 2–5 years We at Coforge are looking for Generative AI Engineer in London, UK. Role Purpose Design, develop, test, and deploy AI-agent workflows that use language models, enterprise data, APIs, and tools to complete business tasks safely and efficiently. Key Responsibilities - Convert business requirements into agent workflows, decision logic, tool calls, and human approval steps. - Build single-agent and basic multi-agent solutions. - Develop prompts, structured outputs, routing, retries, memory, and exception handling. - Implement RAG, grounding, vector search, and context management. - Integrate agents with APIs, databases, enterprise applications, and workflow platforms. - Test accuracy, task completion, safety, latency, cost, and hallucination risk. - Implement guardrails for privacy, permissions, prompt injection, and responsible AI. - Diagnose issues using logs, traces, model responses, and tool-call history. - Develop maintainable Python or TypeScript code and support CI/CD deployment. - Create technical documentation, workflow diagrams, and operational runbooks. Required Skills - Python and/or TypeScript - Generative AI, large language models, prompt engineering, and structured outputs - Agent frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAI - RAG, embeddings, vector databases, and enterprise search - REST APIs, SQL, Git, Docker, cloud services, and CI/CD - AI evaluation, monitoring, security, and responsible-AI practices Qualifications - Bachelor’s degree in Computer Science, Engineering, IT, Data Science, or equivalent experience. - 2–5 years in software, data, automation, or AI engineering. - Experience building at least one generative-AI, RAG, chatbot, automation, or agentic-AI solution. - Experience integrating applications through APIs. Behavioural Competencies - Strong problem-solving and analytical skills - Experimentation and continuous-learning mindset - Good engineering and documentation discipline - Ability to collaborate with technical and business teams - Ownership of development, deployment, and issue resolution - Sound judgement when handling AI risks and limitations Performance Measures - Agent accuracy and task-completion rate - Business adoption and productivity improvement - Reliability, latency, and cost efficiency - Defect and hallucination rates - Delivery against sprint and release commitments - Code quality, test coverage, and documentation