Technical Lead, GenAI & Automation Engineering (Risk Engineering)

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose.

Position Overview
We are seeking a highly experienced Technical Lead in GenAI & Automation Engineering to design and build AI-driven use cases, agentic workflows, and automation solutions that solve complex business and risk challenges.

This role sits at the intersection of AI engineering and risk management, focusing on embedding controls, governance, and risk intelligence directly into GenAI systems (“control as code”).

You will lead the development of production-grade GenAI solutions, leveraging technologies such as LLMs, RAG architectures, vector databases, and modern cloud-native frameworks, while ensuring scalability, reliability, and responsible AI practices.
 

Our Impact

At Freddie Mac, our mission of Making Home Possible drives everything we do. As we advance into an AI-powered future, we are transforming how risk management is designed, implemented, and scaled—moving from manual processes to intelligent, automated, and embedded risk capabilities.

This role is part of a next-generation initiative to reimagine risk management through GenAI and automation, where controls are engineered into systems, not just documented.

Your Impact

In this role, you will design and implement scalable GenAI applications, AI agents, and agentic workflows to solve complex business challenges, while building and optimizing RAG pipelines, vector search solutions, and multi-modal AI integrations. You will develop Python-based microservices and APIs, lead automation framework design, and ensure efficient deployment and high system performance. A key differentiator is embedding risk and control mechanisms directly into GenAI workflows, translating regulatory requirements into scalable code, and partnering with Risk, Security, and Governance teams. You will also design feature engineering and data pipelines, establish feedback loops for continuous improvement, and collaborate with cross-functional teams while providing technical leadership. Success in this role requires engineering excellence, technical depth in Python and AI/ML frameworks, data readiness, a risk-aware mindset, and a commitment to innovation in emerging AI technologies.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field (advanced degree preferred)

• 8–10+ years of experience in software engineering

• 5+ years of experience in AI/ML or GenAI engineering

• Strong proficiency in Python and experience with frameworks such as LangChain or similar

• Experience building LLM-based applications, including RAG and prompt engineering

• Knowledge of APIs, microservices, and distributed systems in cloud environments (AWS preferred)

• Experience with MLOps/DataOps pipelines, observability, and monitoring tools

• Familiarity with vector databases and modern AI architectures

• Experience with LLM evaluation, guardrails, or AI safety mechanisms

• Familiarity with AI governance, model risk, or responsible AI frameworks

• Experience implementing risk, control, or compliance requirements in technical systems

• Experience with platforms such as AWS Bedrock, Azure OpenAI, and GitHub Copilot

Keys to Success in the Role

• Technical Proficiency: Strong Python skills and ability to build scalable microservices

• Quality & Reliability: Deliver robust, well-tested, and production-ready solutions

• Learning Agility: Continuously develop skills in GenAI and emerging technologies

• Collaboration: Work effectively across engineering, product, and risk teams

• Attention to Detail: Ensure high-quality data, outputs, and system performance

Current Freddie Mac employees please apply through the internal career site.

We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal