AI Engineer

Role : Senior AI Engineer – Privacy Location : Bellevue WA 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 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.