Senior AI Engineer – Privacy
Share
Job Location:
Bellevue, WA - USA
Monthly Salary:
Not Disclosed
Posted on:
3 hours ago
Vacancies:
1 Vacancy
Job Summary
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation information technology and services
Position: Senior AI Engineer Privacy
Location: Bellevue WA
Duration: 6 Months
Job Type: Temporary Assignment
Work Type: Onsite
Job Description
- 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 organizations 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 T-Mobile 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.
TekWissen Group is an equal opportunity employer supporting workforce diversity.
