Candidate Skill:
GenAI, RAG, LangChain, LlamaIndex, Python, PyTorch, Azure, Vector DB, Prompt Engineering
Job Description:
We are looking for a Senior Generative AI Engineer to design and deliver production-grade RAG (Retrieval-Augmented Generation) and Agentic AI solutions on Azure. The role involves building scalable AI systems such as chatbots over large document corpora, while ensuring performance, security, observability, and cost optimization. Key Responsibilities Solution Architecture & Delivery Design and implement end-to-end RAG pipelines (ingestion, chunking, embeddings, retrieval) Architect agentic systems with tool/function calling and multi-step workflows Evaluate and choose between LangChain and LlamaIndex based on use cases Data & Retrieval Build ingestion pipelines for multi-format data (PDF, HTML, PPT, Docs) Implement semantic chunking and metadata strategies Manage vector stores (FAISS, Azure AI Search, pgvector, Chroma) Develop hybrid retrieval and re-ranking systems LLM Orchestration & Prompting Design prompt strategies (few-shot, system prompts, tool schemas) Implement prompt versioning, A/B testing, and caching Optimize context window usage and token costs Cloud & API Integration (Azure) Integrate OpenAI / Azure OpenAI / Gemini APIs with retries and rate limits Work with Azure services (AI Search, Functions, Storage, Key Vault, AKS) Implement CI/CD pipelines and deployment strategies Quality, Security & Observability Define and track RAG evaluation metrics (accuracy, groundedness, hallucination rate) Build evaluation pipelines and feedback loops Implement data privacy, PII handling, and security best practices Required Skills Strong Python and PyTorch fundamentals Hands-on experience with LangChain and LlamaIndex Proven experience building RAG pipelines and GenAI applications Expertise in vector databases and retrieval techniques Experience with OpenAI / Azure OpenAI / Gemini APIs Strong understanding of prompt engineering and evaluation strategies Hands-on experience with Azure cloud services Knowledge of data privacy, compliance, and secure AI systems Good to Have Experience with TensorFlow, Keras, or scikit-learn Knowledge of FAISS internals (HNSW, IVFPQ, indexing strategies) Experience with observability (OpenTelemetry, logging, tracing) Exposure to SQL/BI integrations and AI-driven analytics Familiarity with AI guardrails and content safety mechanisms Soft Skills Strong problem-solving and analytical thinking Ability to run PoCs and experiment rapidly Good communication and knowledge-sharing mindset
