Software Engineer, Professional; Gen AI Data Scientist

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. Continue your career journey where your work contributes to a greater purpose.

We are seeking an Software Engineer, Professional - Gen AI (Data) Scientist with a strong focus on Generative AI (Gen AI) to lead the design and development of cutting‑edge AI Agents, Agentic Workflows and Gen AI Applications that solve complex business problems. This role requires advanced proficiency in Prompt Engineering, Large Language Models (LLMs), RAG, Graph RAG, MCP, A2A, multi‑modal AI, Gen AI Patterns, Evaluation Frameworks, Guardrails, data curation, and AWS cloud deployments.

You will serve as a hands‑on Gen AI (data) scientist and critical thought leader, working alongside full stack developers, UX designers, product managers and data engineers to shape and implement enterprise‑grade Gen AI solutions.

At Freddie Mac, we are at the forefront of technological innovation, developing AI solutions that transform complex business challenges into streamlined, automated processes. By leveraging cutting‑edge AI Agents, Agentic Workflows, and Gen AI Applications, we enable businesses to enhance their operational efficiency, make data‑driven decisions, and unlock new opportunities for growth. Our commitment to integrating advanced technologies like LLMs and multi‑modal AI into enterprise solutions ensures that we remain leaders in the AI industry, delivering impactful and sustainable results for our clients.

As Software Engineer, Professional
- Gen AI (Data) Scientist, your role is pivotal in shaping the future of AI‑driven business solutions. You will have the opportunity to design and develop scalable applications that integrate sophisticated AI models, directly influencing how businesses operate and succeed. Your expertise in Automated QA and Python‑based microservices will be crucial in creating robust quality‑controlled frameworks for Gen AI solutions that will help with Governance Approvals.

By collaborating with Gen AI scientists, UX designers, and other cross‑functional teams, you will drive the implementation of enterprise‑grade Gen AI solutions, ensuring they meet the highest standards of performance and reliability.

• Design and implement scalable AI Agents, Agentic Workflows and GenAI applications to address diverse and complex business use cases.
• Evaluate and adapt models such as Claude (Anthropic), Azure OpenAI, and open‑source alternatives for business use cases.
• Train, Fine‑tune, optimise and Test lightweight Large Language Models (LLMs) to address diverse and complex business use cases.
• Design and deploy Retrieval‑Augmented Generation (RAG) and Graph RAG systems using vector databases and knowledge bases.
• Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge Base/Elastic.
• Implement solutions leveraging MCP (Model Context Protocol) and A2A (Agent‑to‑Agent) communication.
• Build and maintain Jupyter‑based notebooks using platforms like Sage Maker and MLFlow/Kubeflow on Kubernetes (EKS).
• Collaborate with cross‑functional teams of UI and microservice engineers, designers, and data engineers to build full‑stack Gen AI experiences.
• Integrate GenAI solutions with enterprise platforms via API‑based methods and GenAI standardized patterns.
• Establish and enforce validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails for production‑ready deployment.
• Design & build robust ingestion pipelines that extract, chunk, enrich, and anonymise data from PDFs, video, and audio sources for use in LLM‑powered workflows—leveraging best practices like semantic chunking and privacy controls.
• Orchestrate multimodal pipelines using scalable frameworks (e.g., Apache Spark, PySpark) for automated ETL/ELT workflows appropriate for unstructured media.
• Implement embeddings drives—map media content to vector representations using embedding models, and integrate with vector stores (AWS Knowledge…