Senior AI Engineer Privacy

Role: Senior AI Engineer Privacy

Location: Bellevue, WA

FAANG & Product & Top tier 1 Companies Mandatory: Yes

Must have skills

Skill 1 7yrs of exp AI Engineer Privacy

Skill 2 7yrs of exp Azure Data Factory, Azure, GitLab

Skill 3 5yrs of exp Databricks Snowflake

The Senior AI Engineer Privacy will design, build, and operationalize AI and agentic systems that power Client data privacy platform at scale. Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply large language models (LLMs), retrieval-augmented generation (RAG), multi-agent orchestration, and foundation model capabilities to automate, enhance, and scale privacy operations including Data Subject Request (DSR) processing, consent management, regulatory compliance monitoring, and privacy impact assessment workflows across a customer base of over 100 million.

You will collaborate with data engineers, full stack engineers, privacy product managers, and legal and compliance teams to deliver production-grade AI solutions. You will apply responsible AI principles, implement human-in-the-loop controls, and ensure audit logging and observability across AI-assisted privacy workflows. Your work will directly shape how Client meets its obligations under CCPA, CPRA, TCPA, and other state and federal privacy regulations.

AI Agent & LLM Engineering

• Design and build multi-agent systems, orchestration layers, and agentic workflows using frameworks such as LangChain, LangGraph, Google ADK, or equivalent.
• Develop and operationalize RAG (Retrieval-Augmented Generation) pipelines integrating LLMs (e.g. Claude, Gemini, GPT-4) into production privacy applications.
• Implement structured prompting, decision workflows, and tool orchestration including MCP (Model Context Protocol)-based architectures for autonomous agent systems.
• Build AI-powered automation for privacy operations including intelligent DSR routing, threshold monitoring, agentic data quality checks, and automated regulatory notifications.
• Enable human-in-the-loop controls and escalation paths for AI-assisted decisions in sensitive privacy workflows.

Data & ML Engineering

• Build and optimize data pipelines using Azure Data Factory, Databricks, Snowflake, or PySpark to support AI model training, fine-tuning, and inference.
• Apply prompt engineering, few-shot learning, and fine-tuning techniques to adapt foundation models for privacy-specific use cases.
• Implement vector databases and embedding strategies to power RAG pipelines over Client internal privacy knowledge bases and policy documents.
• Ensure data quality, lineage, and governance standards are maintained across all AI training and inference pipelines.

Cloud & MLOps

• Deploy and manage AI workloads on Azure or AWS, including serverless inference endpoints, container registries, and GPU/compute resources.
• Build and maintain CI/CD pipelines for AI model deployment using GitLab or Azure DevOps, applying MLOps best practices.
• Implement monitoring, alerting, and performance tracking for production AI models and agent systems using Splunk, AppDynamics, or Grafana.
• Apply containerization (Docker) and orchestration (Kubernetes) to ensure scalable and reliable AI service deployments.

Responsible AI & Compliance

• Implement responsible AI principles including fairness, transparency, and explainability across all AI systems used in privacy operations.
• Ensure AI-assisted workflows comply with CCPA, CPRA, TCPA, and other applicable state and federal privacy regulations.
• Design and maintain audit trails and human-in-the-loop checkpoints for AI decisions affecting consumer privacy rights.
• Collaborate with legal, compliance, and privacy operations teams to translate regulatory requirements into AI solution guardrails and constraints.

Technical Leadership & Collaboration

• Partner with data engineers, full stack engineers, product managers, and privacy stakeholders to deliver end-to-end AI-powered privacy solutions.
• Mentor junior engineers on AI/ML engineering practices, agentic patterns, and responsible AI design principles.
• Produce clear technical documentation, architecture diagrams, and model cards for AI systems in production.
• Contribute to internal accelerators, reusable AI component libraries, and the broader engineering community of practice.