Generative AI Engineer

Role: Generative AI Engineer

Location: London, UKWork mode: Hybrid - 3 days weekly from officeType: Fulltime-PermanentExperience: 2–5 yearsWe at Coforge are looking for Generative AI Engineer in London, UK.Role PurposeDesign, develop, test, and deploy AI-agent workflows that use language models, enterprise data, APIs, and tools to complete business tasks safely and efficiently.Key ResponsibilitiesConvert 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 SkillsPython and/or TypeScriptGenerative AI, large language models, prompt engineering, and structured outputsAgent frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAIRAG, embeddings, vector databases, and enterprise searchREST APIs, SQL, Git, Docker, cloud services, and CI/CDAI evaluation, monitoring, security, and responsible-AI practicesQualificationsBachelor’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 CompetenciesStrong problem-solving and analytical skillsExperimentation and continuous-learning mindsetGood engineering and documentation disciplineAbility to collaborate with technical and business teamsOwnership of development, deployment, and issue resolutionSound judgement when handling AI risks and limitationsPerformance MeasuresAgent accuracy and task-completion rateBusiness adoption and productivity improvementReliability, latency, and cost efficiencyDefect and hallucination ratesDelivery against sprint and release commitmentsCode quality, test coverage, and documentation