Principal Engineer, Data Analytics Engineering job opportunity at SanDisk.

Home Jobs In India Tag: Data Analytics Engineering Principal Engineer, Data Analytics Engineering position at SanDisk


Date2026-04-10T09:17:27.159Z bot
SanDisk Principal Engineer, Data Analytics Engineering
Experience: General
Pattern: Full-time
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Data Analytics Engineering

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degreeGeneral
loacation Bengaluru, Karnataka, India
loacation Bengaluru, Kar..........India
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Job DescriptionJob Description As a GenAI Solution Architect, you will design and implement enterprise-grade Generative AI solutions that seamlessly integrate with business applications and workflows. This role spans end-to-end architecture—from building & maintaining - GenAI pipelines, prompt engineering strategies, and multi-LLM gateways to managing data lake houses, retrieval & knowledge governance frameworks. You will develop ontologies, taxonomies, and agentic workflows for autonomous reasoning while ensuring compliance, observability, and cost optimization. The ideal candidate combines deep expertise in AI/ML systems, data engineering, and enterprise integration to deliver scalable, secure, and efficient GenAI solutions that transform knowledge management and decision-making across the organization. Preference will be given to candidates with experience in both leveraging industry-leading solutions and building custom GenAI solutions from the ground upKey Responsibilities: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: Python2. 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.3. 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.4. 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.5. 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 analysis6. 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.7. 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.

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