Staff Data Analytics Engineer

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. 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. 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. 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. 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. 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. 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 and operational metrics; Make Build vs. Buy decisions based on cost-benefit analysis

Responsibilities

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); 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; 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; 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; 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; Establish frameworks for semantic registry, prompt feedback, and knowledge harvesting; Ensure compliance, normalization, and promotion of LLM outputs as enterprise knowledge; Implement observability frameworks for GenAI systems (performance, latency, drift); Monitor and optimize token usage, inference cost, and model efficiency; Maintain dashboards for usage analytics and operational metrics; Make Build vs. Buy decisions based on cost-benefit analysis

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Sciences, or related fields; 8-12+ years in technology roles, with at least 3-5 years in AI/ML solution architecture or enterprise AI implementation; Python

Nice to have

Certifications in Cloud Architecture; Experience with Agentic frameworks; Excellent communication and stakeholder management skills