Software Engineering, Senior Director

Freddie Mac•McLean, VA

1d•$228,000 - $342,000

About The Position

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: Do you want your engineering leadership to shape how an entire enterprise puts generative AI to work — safely, at scale, and for real impact? At Freddie Mac, our mission of Making Home Possible is at the core of everything we do, and our Corporate Technology Strategy team is building FredAI, our internal enterprise GenAI platform and portfolio of AI-enabled products. We're searching for a hands-on, visionary engineering leader to own the technical strategy and delivery that turns cutting-edge foundation models into measurable business outcomes. Apply now and learn why there's #MoreAtFreddieMac! Our Impact: We build the secure, scalable platforms and reusable services that bring generative AI capabilities — including the company's internal chatbot and AI-enabled products — to teams across the enterprise. We translate the Freddie Mac CIO vision for GenAI into an executable engineering roadmap, setting the reference architectures, standards, and shared frameworks that let multiple product teams deliver consistently and responsibly. We partner across Architecture, Cybersecurity, Data, Model Risk, and Cloud Engineering to ensure every solution meets enterprise expectations for identity, data protection, auditability, resiliency, and responsible AI.

Requirements

15+ years of software, data, or machine learning engineering experience, including significant technical and people leadership responsibility (4+ years leading engineering teams/managers).
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent experience (advanced degree preferred).
Proven experience architecting and delivering production GenAI or AI/ML systems at enterprise scale, with recent hands-on coding experience.
Strong proficiency in modern cloud-native engineering (Python, TypeScript/JavaScript, or Java; APIs, microservices, containers, distributed systems, CI/CD, IaC).
Hands-on knowledge of LLM application patterns — prompting, RAG, embeddings, vector search, orchestration, tool calling, agents, structured outputs, and model evaluation.
Experience designing for security, privacy, identity, data governance, resiliency, auditability, and responsible AI in a regulated enterprise environment.

Nice To Haves

A builder's mindset — you love solving the hardest engineering problems hands-on, not just directing from the sidelines.
A natural influencer who can rally engineering, security, data, risk, and business partners around a shared technical vision.
Comfortable operating in the space of "no longer" and "not yet" — translating fast-moving GenAI advances into practical, governed capabilities.
Calm under pressure, with strong operational discipline and a bias toward measurable outcomes.
A curious, continuous learner who stays current on emerging AI technologies — and a great teammate with a sense of humor.

Responsibilities

Lead the end-to-end engineering strategy, architecture, and delivery of FredAI, building production GenAI applications using foundation models, RAG, embeddings, vector search, orchestration, tool calling, and agentic workflows.
Build a reusable platform of reference architectures, SDKs, shared services, APIs, and integration patterns that accelerate delivery across multiple product teams.
Implement rigorous evaluation, testing, and LLMOps practices — offline benchmarks, regression suites, groundedness and hallucination measures, CI/CD, versioning, observability, tracing, incident response, and rollback.
Engineer secure, permission-aware solutions that enforce identity, entitlements, data boundaries, privacy, human approvals, audit logging, and responsible AI policies.
Define model and platform integration patterns, including model selection and routing, structured outputs, caching, rate limiting, fallback strategies, token budgets, circuit-breaker controls, and cost management.
Partner with cloud, platform, data, and security teams, and with the GenAI Program Manager, to integrate GenAI services with enterprise systems and translate business needs into governed, high-value capabilities.
Lead, coach, and grow a high-performing, multidisciplinary team of GenAI, software, platform, and quality engineers, setting clear expectations, career paths, and a culture of ownership and experimentation.
Manage engineering and product-quality outcomes — availability, latency, throughput, task completion, adoption, developer productivity, and cost per successful outcome.