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: 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 hands-on technical leadership in scalable AI agent design, cutting-edge model development, 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.
Our Impact: 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.
Your Impact: 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.
Technical Leadership & Solution Architecture - GenAI Agentic Design:
Architect and implement scalable AI agents, agentic workflows, and GenAI applications tailored for Freddie Mac's most complex business challenges. - Model Development & Optimization:
Develop, fine-tune, and optimize lightweight LLMs; lead the evaluation and adaptation of models such as Claude (Anthropic), Azure OpenAI, and open-source alternatives. - RAG & GraphRAG Systems:
Design and deploy Retrieval-Augmented Generation (RAG) and GraphRAG solutions using vector databases and enterprise knowledge bases (e.g., AWS Bedrock Knowledge Base, Elastic). - Enterprise Data Curation:
Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge Base/Elastic to support robust knowledge retrieval. - Agent Communication:
Implement solutions leveraging Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication patterns. - Notebook Infrastructure:
Build and maintain Jupyter-based notebooks using platforms such as SageMaker and MLFlow/Kubeflow on Kubernetes (EKS). - Full-Stack Collaboration:
Partner with UI engineers, microservice developers, designers, and data engineers to deliver seamless, full-stack GenAI experiences. - API Integration:
Integrate GenAI solutions with enterprise platforms using API-based methods and standardized GenAI patterns. - Production Validation:
Establish and enforce validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails for production-ready deployment. - Data Ingestion Pipelines:
Design and build robust ingestion pipelines to extract, chunk, enrich, and anonymize data from PDFs, video, and audio for LLM-powered workflows, leveraging semantic chunking and privacy best practices. - Multimodal Pipelines:
Orchestrate multi-modal pipelines using scalable frameworks (e.g., Apache Spark, PySpark) for automated ETL/ELT workflows on unstructured media. - Embeddings & Vector Stores:
Implement embeddings mapping media content to vector representations and integrate with vector stores (AWS Knowledge Base, Elastic, Mongo Atlas) to support advanced RAG architectures.
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.
Qualifications: - Bachelor's in computer science, Artificial Intelligence (AI), Data Science, or related field. Master's in computer science or advanced studies preferred.
- 8+ years of experience in Software Engineering with 5 yrs. in data science, 1-2 yrs. in applied GenAI or LLM-based solutions.
- 2+ yrs. of leadership experience.
- Demonstrated experience leading cross-functional agile teams combining data scientists and full stack engineers.
- Deep expertise in prompt engineering, fine-tuning, RAG, Graph RAG, vector databases (AWS Knowledgebase, Elastic), and multi-modal models.
- Proven experience with cloud-native AI development (AWS SageMaker, Bedrock, MLFlow, Kubeflow on EKS).
- Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.).
- Deep understanding of Gen AI system patterns, architectural best practices, and evaluation frameworks for bias mitigation and safety.
- Experience with embedding models, vector stores, multimodal data pipelines, and production-grade validation.
- Excellent communication skills; ability to translate technical concepts for non-technical stakeholders.
Preferred Qualifications - Experience in regulated financial environments with compliance automation.
- Prior work implementing agentic workflows and AI-powered enterprise platforms.
Keys to Success in this Role: Deliver Predictably: Ship high-quality, secure, and compliant Agentic AI solutions on time.
Innovate Rapidly: Incubate business ideas through experimentation, develop MVPs, and scale them to production.
Lead Technically: Architect scalable agentic AI systems and stay ahead of technology trends.
Drive Business Impact: Align AI initiatives with business goals and clearly communicate ROI and outcomes.
Engage Stakeholders: Build strong partnerships and communicate proactively across teams.
Empower Your Team: Foster a culture of psychological safety, ownership, and continuous growth.
Balance Innovation & Compliance: Deliver advanced solutions while meeting regulatory and security standards.
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, state or local laws. We will ensure that individuals are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
A safe and secure environment is critical to Freddie Mac's business. This includes employee commitment to our acceptable use policy, applying a vigilance-first approach to work, supporting regulatory mandates, and using best practices to protect Freddie Mac from potential threats and risk. Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.
CA Applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit and register with our referral code: MAC.
Time-type:Full time
FLSA Status:Exempt
Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.
This position has an annualized market-based salary range of $153,000 - $229,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.
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