AI Analyst

Role & responsibilities

• Design, build, and optimize enterprise Generative AI applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
• Validate AI-generated responses against business data sources (e.g., Looker Explore/semantic models) to ensure factual accuracy and semantic alignment.
• Develop and optimize prompt engineering strategies to improve response quality, consistency, and reduce hallucinations.
• Build and enhance RAG pipelines using vector databases, embeddings, semantic search, and retrieval optimization techniques.
• Implement AI evaluation frameworks to measure response accuracy, relevance, faithfulness, latency, and overall LLM performance.
• Collaborate with data engineering, analytics, and business teams to integrate AI capabilities into enterprise applications.
• Develop scalable backend services and APIs using Python and FastAPI for AI solutions.
• Monitor production AI systems using observability tools, perform root cause analysis, and continuously improve model performance.
• Ensure AI solutions follow Responsible AI, security, governance, and enterprise best practices.

Preferred candidate profile

• 610+ years of overall software development experience with 2–4+ years in Generative AI/LLM engineering.
• Strong hands-on expertise in Python and backend API development (FastAPI preferred).
• Experience building production-grade LLM, RAG, and Agentic AI applications.
• Hands-on experience with LangChain/LangGraph or similar orchestration frameworks.
• Experience working with Vector Databases such as Azure AI Search, Pinecone, ChromaDB, FAISS, or OpenSearch.
• Strong understanding of Prompt Engineering, embeddings, semantic search, hybrid retrieval, and reranking techniques.
• Experience implementing AI Evaluation/Validation frameworks (RAGAS, DeepEval, LLM-as-a-Judge, hallucination detection, response quality evaluation).
• Familiarity with Azure OpenAI, AWS Bedrock, OpenAI APIs, or similar enterprise AI platforms.
• Strong knowledge of SQL and enterprise data models.
• Experience with Docker, Git, CI/CD, and cloud deployment is preferred.
• Excellent analytical, debugging, stakeholder communication, and problem-solving skills.
• Experience working in enterprise, banking, financial services, or large-scale production environments is an added advantage.