Principal Engineer, Data Analytics Engineering

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape.

Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.

Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.

Job 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 up

Key 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: Python

2. 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 analysis

6. 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.

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

 

Sandisk thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

Sandisk is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.