Gen AI Engineering Manager

Freddie Mac • McLean, VA

1d

About The Position

Freddie Mac is seeking a visionary Gen AI Engineering Manager to architect and deliver next-generation GenAI applications, agentic workflows, and AI-powered platforms. You will lead a cross-functional team of data scientists and full stack engineers to address diverse and complex business use cases. Your role combines technical leadership in scalable AI agent design, production-grade deployment, and collaborative full-stack solution delivery. You will champion flawless execution, drive strategic innovation, master stakeholder engagement, and build an agentic, high-performance engineering culture. Through our Agentic AI initiatives, Freddie Mac's Single-Family Acquisitions is transforming its business, delivering real value for our customers and stakeholders, and preparing our organization for the future of intelligent, autonomous technology.

Requirements

• Bachelors in computer science, Artificial Intelligence (AI), Data Science, or related field. Advanced degree preferred.
• 8-10 years of experience in Software Engineering /data science
• 5+ yrs. of leadership experience
• Demonstrated experience leading cross-functional agile teams combining data scientists and full stack engineers.
• Excellent communication skills; ability to translate technical concepts for non-technical stakeholders.

Nice To Haves

• Experience in regulated financial environments with compliance automation.
• Prior work implementing agentic workflows and AI-powered enterprise platforms.
• Familiarity with Gen AI system patterns, architectural best practices, and evaluation frameworks for bias mitigation and safety.
• Familiarity with embedding models, vector stores, multimodal data pipelines, and production-grade validation.
• 1-2 yrs. in applied GenAI or LLM-based solutions.

Responsibilities

• Business Idea Incubation, MVP Development, and Productionalization Idea Incubation & Experimentation: Collaborate with business stakeholders to identify and incubate innovative ideas by leveraging data science and GenAI experimentation and research. Rapidly prototype solutions to validate hypotheses and quantify business impact.
• MVP Development: Lead the development of Minimum Viable Products (MVPs) based on validated experiments, ensuring the MVP delivers tangible value and is architected for scalability and compliance.
• Productionalization: Drive the transition from MVP to scalable, production-ready GenAI solutions. Enforce best practices for code quality, validation, security, and operational excellence. Ensure solutions are robust, efficient, and aligned with enterprise standards.
• Gen AI Familiarity Familiarity with Gen AI, AI Agents, Model Context Protocol, Retrieval-Augmented Generation (RAG)
• Execution Excellence Establish agile, empirically driven SDLC and manage delivery metrics (cycle time, lead time). Enforce a rigorous "Definition of Done" (code review, automated security/compliance scans, unit testing>80>80coverage, QA validation, deployment). Integrate internal platforms to automate compliance and reduce manual toil. Drive predictable engineering flow: story pointing, WIP management, epic deconstruction for AI enablement. Lead blameless retrospectives and leverage AI tools for continuous improvement. Track and visualize key metrics (DORA, lead time, deployment frequency, uptime).
• Strategic Impact Identify and champion technology-driven opportunities (GenAI, ML, cloud, data platforms). Build business cases that quantify ROI, TCO, and measurable impact. Maintain external focus on industry trends and competitive landscape. Collaborate with Risk, Compliance, and InfoSec to innovate safely. Set quarterly OKRs and prioritize using a portfolio approach (Enablement vs. Targeted Solutions). Present quarterly "State of the Union" and connect stories to strategy.
• Stakeholder Engagement Proactively communicate risks, changes, and options to business, technology, and compliance partners. Use documentation for clarity and alignment, leverage AI tooling for communication. Partner with product and business stakeholders to present crisp options and trade-offs. Treat dependencies as contracts and create shared goals (OKRs) for cross-functional initiatives. Proactively address compliance and build enablement tooling.
• Team Building & Growth Foster an agentic, psychologically safe team culture. Set explicit expectations and manage performance with structured feedback (SBI model). Conduct growth-focused 1-on-1s and create opportunities for ownership and development. Lead hiring and onboarding with clear job descriptions and structured 30-60-90-day plans.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager