Senior AI Engineer Privacy

Role :
Senior AI Engineer Privacy ( 22441-1)
Duration: 6-12+ Months Contract
Location :
Bellevue, WA - Remote

This is for Data Governance - Privacy under Travis Bakeman

must have skills
skill 1 7yrs of exp AI Engineer Privacy
skill 2 7yrs of exp Azure Data Factory, Azure , Git Lab
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 edded 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 Telecommunication 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 Lang Chain, Lang Graph, 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 Git Lab or Azure Dev Ops, applying MLOps best practices.
" Implement monitoring, alerting, and performance tracking for production AI models and agent systems using Splunk, App Dynamics, 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.