Staff Data Analytics Engineer

Job Description 1.GenAI Development & Integration - Design and implement GenAI workflows for enterprise use cases. - Develop prompt engineering strategies and feedback loops for LLM optimization. - Capture and normalize LLM interactions into reusable Knowledge Artifacts. - Integrate GenAI systems into enterprise apps (APIs, microservices, workflow engines) - Programming languages: Python 2. Model Gateway & Multi-LLM Strategy - Architect model gateways to access multiple LLMs (OpenAI, Anthropic, Cohere, etc.). - Dynamically select models based on accuracy vs. cost trade-offs. - Benchmark and evaluate models for enterprise-grade performance. 3. Agentic Workflows - Design and implement agent-based orchestration for multi-step reasoning and autonomous task execution. - Design and implement agentic workflows using industry-standard frameworks for autonomous task orchestration and multi-step reasoning. - Ensure safe and controlled execution of agentic pipelines across enterprise systems via constraints, policies, and fallback paths. 4. Data Lakehouse & Knowledge Management - Architect and maintain Lakehouse environments for structured and unstructured data. - Implement pipelines for document parsing, chunking, and vectorization. - Maintain knowledge stores, indexing, metadata governance - Enable semantic search and retrieval using embeddings and vector databases. 5. Ontology & Taxonomy Engineering - Build and maintain domain-specific ontologies and taxonomies. - Establish taxonomy governance and versioning. - Connect semantic registries with LLM learning cycles. - Enable knowledge distillation from human/LLM feedback. 6. AI Governance & Knowledge Distillation - Establish frameworks for semantic registry, prompt feedback, and knowledge harvesting. - Ensure compliance, normalization, and promotion of LLM outputs as enterprise knowledge. 7. Observability & Cost Optimization - Implement observability frameworks for GenAI systems (performance, latency, drift). - Monitor and optimize token usage, inference cost, and model efficiency. - Maintain dashboards for usage analytics & operational metrics. - Make Build vs. Buy decisions based on cost-benefit analysis