Role : Senior AI Engineer – Privacy Bellevue WA
Arundeep Rayabandi
Role : Senior AI Engineer – Privacy
Location : Bellevue WA
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 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.
Thanks & Regards,
Arundeep Rayabandi
Senior Talent Acquisition Specialist
American IT Systems
1116 S Walton Blvd, Suite 113 Bentonville, AR 72712
https://americanitsystems.com/
linkedin.com/in/arundeep-r-713a421b8
Phone No:::(479) 265 8527
