GenAI Engineer – RAG Systems & AI Transformation

About the Role We are seeking a highly skilled and forward-thinkingGenAI Engineerto join our AI innovation team. This role is ideal for someone with deep technical expertise inGenerative AI , a strong foundation inPython programming , and a passion for drivingenterprise AI transformation . You will be instrumental in designing, developing, and deployingadvanced Retrieval-Augmented Generation (RAG) systems . You’ll also play a pivotal role inenabling our internal workforceto embrace and adopt AI technologies.Key Responsibilities Architect and implementscalable RAG systems using Python and modern GenAI tools. Buildcustom pipelinesfor document ingestion, chunking strategies, and embedding generation. Working knowledge inLlamaIndexis preferable. Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions Evaluate and implement differentembedding models(OpenAI, Azure OpenAI, Cohere, etc.) andchunking strategies(fixed-size, semantic-aware, overlap-based). Create and optimizeindexing strategies(vector, hybrid, keyword-based, hierarchical) for performance and accuracy. Work withAzure AI Services , particularly Azure Cognitive Search and OpenAI integration, to deploy end-to-end AI applications. Collaborate closely with cross-functional teams including data engineers, product managers, and domain experts. ConductAI enablement sessions , workshops, and hands-on labs to upskill internal teams on GenAI usage and best practices. Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems.Required Qualifications 5+ years of experiencein software engineering, with strong expertise inPython . 2+ years ofproven track record of building and deployingRAG-based GenAI solutions . Hands-on experience withLlamaIndex ,LangChain , or equivalent frameworks. Familiarity with prompt engineering, prompt tuning, and managingcustom Copilot extensions . Strong understanding ofLLMs , vector databases (like FAISS, Pinecone, Azure Cognitive Search), andembedding techniques . Solid knowledge ofAzure AI , cloud deployment, and enterprise integration strategies. Proficiency with version control and collaborative development usingGitHub .Preferred Qualifications Experience withtransformer models , fine-tuning, and inference optimization. Working knowledge ofMS Azure . Contributions to open-source GenAI projects or technical blogs. Previous experience intraining or mentoringteams on AI adoption.

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