AI/ML & Gen AI -AI Data Governance Engineer

Hello Everyone,

Hope you are doing good!!!!

My name is Pavan and I work with SPAR Information System., I have a great opportunity for you, please find the job details below, if you are interested in applying please send me your updated resume and best time for you to discuss about this opportunity in details.

Sr Engineer, AI Data Governance (AI/ML & GenAI)
Location : Bellevue WA

Duration: Long term contract

Skill set: artificial intelligence, retrieval-augmented generation, nlp, lang chain, mlflow, docker, python

The Sr. Engineer, AI - Data Governance will design, build, and operationalize AI and machine learning systems that power Client enterprise Data Governance program at scale. Embedded within the Data & Intelligence organization, this engineer will apply large language models (LLMs), retrieval-augmented generation (RAG), machine learning, multi-agent orchestration, and foundation model capabilities to automate, enhance, and dramatically scale governance operations - including automated data classification, intelligent metadata discovery, lineage generation, data quality automation, and natural language data discovery across.
This is a uniquely high-impact role: the AI solutions you build will directly determine how well client enterprise knows its own data - what it is, where it lives, who owns it, how it's being used, and whether it's trustworthy.
You will collaborate with Data Governance platform engineers, data engineers, product managers, and governance stakeholders to deliver production-grade AI solutions that make governance smarter, faster, and scalable across the enterprise. Experience in the Data Governance space is a plus but not required. What is required is deep hands-on experience building production ML and Generative AI systems, combined with a solid understanding of data, data warehousing concepts, and a genuine curiosity about how enterprise data governance works and why it matters.
What You'll Do Automated Data Classification & Semantic Mapping Design and build ML and LLM-powered data classification systems that can identify the nature and sensitivity of data across client 4,000 applications - mapping physical data assets to business glossary terms, data domains, and sensitivity classifications at scale. Apply NLP, embedding strategies, and fine-tuned foundation models to analyze schema metadata, column names, sample values, and contextual signals to infer data meaning without requiring manual review. Build feedback loops and active learning mechanisms so classification models improve continuously as governance stewards validate or correct suggestions. Integrate classification outputs into client Data Governance platforms (Collibra, Ataccama, OpenMetadata, Securiti.ai) via APIs and automated workflow triggers.
Intelligent Data Discovery & Natural Language Search Build conversational AI and chatbot-style interfaces that allow business users, analysts, and stewards to find data using plain language questions - powered by RAG pipelines over client governance metadata, business glossary, and data catalog. Implement vector databases and embedding strategies to index and retrieve governance knowledge - including data definitions, data lineage, quality metrics, and business context - for LLM-powered Q&A and discovery experiences.
Design intelligent recommendation engines that surface relevant datasets, related assets, and suggested data owners based on natural language intent. Lineage Generation & Gap Filling Design AI-assisted approaches to infer, generate, and complete data lineage where automated capture is partial or missing - leveraging code analysis, SQL parsing, metadata signals, and LLM reasoning. Build models that can identify likely lineage relationships between datasets across disparate platforms (Databricks, Azure, Fabric, DBT) based on schema similarity, naming patterns, and usage history. Integrate lineage generation outputs into governance platforms and validate recommendations with data engineers and stewards through human-in-the-loop workflows. Data Quality Automation & Recommendation Develop AI-powered systems that can analyze datasets and recommend appropriate data quality rules, thresholds, and checks based on the nature of the data, historical patterns, and business context. Build agentic workflows that can automatically apply approved data quality checks across governed d

Thanks & Regards,

Pavan Raikhelkar

LEAD TALENT ACQUISITION SPECIALIST

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