Senior AI & Data Governance Engineer-ii (hybrid in Bangalore )

Smartsheet Smartsheet · Seattle · India · Business Intelligence & Ops

This role focuses on developing and enforcing AI and Data Governance frameworks, ensuring compliance with data privacy laws, security regulations, and AI regulations. It involves implementing tools for data lineage, metadata management, quality frameworks, and auditing AI models for bias and transparency. The goal is to manage risks in AI and data pipelines, define data stewardship roles, and embed security and privacy into AI workflows.

What you'd actually do

  1. Develop and enforce AI&Data governance frameworks, including bias detection, explainability, and model lifecycle management
  2. Ensure adherence to data privacy laws and security regulations, managing risks in AI and data pipelines
  3. Implement data lineage, metadata, data management, and quality frameworks
  4. Implementing tools to track data from its source to the final AI output. If a model breaks, you need to know exactly which data point caused the drift
  5. Designing and enforcing the rules for how data is collected, stored, and used—specifically focusing on data privacy (GDPR, CCPA) and AI regulations (like the EU AI Act)

Skills

Required

  • Experience in AI Governance across foundational pillars like AI Organization, Legal, Regulatory Compliance, Ethics, Transparency, Interoperability, Data/AIOps Infrastructure, AI protection and Security
  • AI Risk Assessment & Management, Auditability & Transparency, AI Policy Development
  • Databricks AI Governance Framework (DAGF), Databricks Security Framework (DASF), MLFlow, Unity Catalog, RAG Governance
  • Python
  • SQL
  • Cloud Platforms (AWS, Azure, or GCP)
  • In-depth understanding of AI, GenAI, LLMs, data quality assessment, and metadata management
  • Regulatory Knowledge in GDPR, EU AI Act, and industry-specific regulations (e.g., HIPAA, Financial Services)
  • Lineage Tracking with automatic capture of end-to-end data flow (from ETL to BI/AI) to understand dependencies
  • Auditability with centralized logs to allow compliance teams to monitor access and usage of data and AI models
  • Knowledge of tools for Model Auditing towards Bias detection,, monitoring, and mitigating bias
  • Understanding of data masking, encryption, and SOC2

Nice to have

  • Experience in AWS hosted data platform is preferable
  • Certifications like CIPP (Privacy), CDMP (Data Management), or specialized AI ethics certifications is preferable
  • Enterprise SaaS software solutions with high availability and scalability

What the JD emphasized

  • AI Governance
  • data privacy
  • security regulations
  • AI regulations
  • bias detection
  • explainability
  • transparency
  • Responsible AI

Other signals

  • AI governance frameworks
  • bias detection
  • explainability
  • model lifecycle management
  • data privacy laws
  • security regulations
  • AI regulations (EU AI Act)
  • Responsible AI standards
  • algorithmic bias auditing
  • transparency
  • ethical implications of automated decision-making
  • Shadow AI identification
  • third-party LLM provider risk management