Role Snapshot PRIMARY FOCUS AI product engineeringCORE STACK Python FastAPIFlaskAI EXPERTISE LLMs RAGDELIVERY Production ownership Also looking at P2 with 4 5 Years of Experience Role Overview We are seeking a handson Senior AI Software Engineer to design build deploy and operate productiongrade Generative AI capabilities This role is ideal for an engineer who combines strong backend fundamentals with practical experience building solutions spanning Generative AI RAG and Agentic AI Key Responsibilities Design and develop Python services that integrate with leading LLM APIs including OpenAI Anthropic or Amazon Bedrock Build endtoend RAG pipelines covering document ingestion chunking embeddings vector search retrieval prompt construction and response generation Build agentic AI capabilities incorporating tool execution workflow orchestration memory guardrails and MCPbased integrations Implement LLMOps practices including observability telemetry performance monitoring and cost optimisation Develop robust REST APIs and backend services using FastAPI or Flask Improve response quality by diagnosing prompt behavior retrieval relevance hallucinations context limitations and token constraints Engineer production resilience through retries ratelimit handling error management observability and operational support Deploy AIenabled features to cloud environments and take ownership of reliability maintainability and performance Collaborate with product architecture engineering and quality teams to translate use cases into secure usable and testable solutions Contribute to code reviews engineering standards technical documentation and continuous improvement of the AI delivery platform Leverage AI pair programming tools GitHub Copilot Cursor AI Gemini Code Assist to accelerate development code conversion and version upgrades
Required Qualifications
6 years of professional experience in Python backend or software engineering
At least 1 year of handson experience delivering LLMenabled features or applications to production
Experience with agent frameworks such as Microsoft Agent Framework MAF LangChainLangGraph AutoGen or CrewAI
Demonstrated experience building RAG pipelines using embeddings vector databases retrieval strategies and prompt orchestration
Strong proficiency in Python and practical experience with FastAPI or Flask
Production experience integrating LLM APIs such as OpenAI Anthropic or Amazon Bedrock
Practical understanding of prompt debugging contextwindow constraints token management hallucination mitigation and model response evaluation
A track record of shipping userfacing features and supporting them in production
Strong problemsolving debugging communication and crossfunctional collaboration skills Preferred Qualifications Experience with LangChain or LlamaIndex Experience with vector databases such as Pinecone Weaviate or ChromaDB Experience creating rapid prototypes or demonstrations using Streamlit Cloud deployment experience on AWS Microsoft Azure or Google Cloud Platform Familiarity with CICD containerization monitoring security controls and automated testing for AI services What Success Looks Like AI features are delivered as working maintainable product capabilities not just prototypes Services remain reliable under production traffic rate limits failures and changing model behavior Response quality improves through systematic evaluation retrieval tuning prompt refinement and defect resolution Code is clear testable documented and aligned with engineering standards IDEAL CANDIDATE A handson engineer who enjoys building debugging and operating AI features with a solid bias toward working software and measurable outcomes
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