Description

• 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

Key Skills

Education

Bachelor's degree