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.

Skilled 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Staff Data Analytics Engineer (Bengaluru)
🏢 Western Digital
📍 Bengaluru