Senior AI Engineer – Privacy and Data Governance
About The Position
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.
Requirements
- 7 years of experience as an AI Engineer with a focus on Privacy
- 7 years of experience with Azure Data Factory, Azure, and GitLab
- 5 years of experience with Databricks and Snowflake
Nice To Haves
- Experience with LangChain, LangGraph, Google ADK, or equivalent frameworks
- Experience with LLMs such as Claude, Gemini, GPT-4
- Experience with MCP (Model Context Protocol)-based architectures
- Experience with vector databases and embedding strategies
- Experience deploying and managing AI workloads on Azure or AWS
- Experience with CI/CD pipelines using Azure DevOps
- Experience with monitoring tools like Splunk, AppDynamics, or Grafana
- Experience with Docker and Kubernetes
- Knowledge of CCPA, CPRA, TCPA, and other privacy regulations
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
