Staff Data Analytics Engineer

Responsibilities

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 & operational metrics.
• Make Build vs. Buy decisions based on cost-benefit analysis

Qualifications

Educational Background

• Bachelor's or Master's degree in Computer Science, Data Sciences, or related fields.

Professional Background

• 8-12+ years in technology roles, with at least 3-5 years in AI/ML solution architecture or enterprise AI implementation.

Preferred Skills

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

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