Freddie Mac

Freddie Mac

GenAI Software Engineer, Technical Lead

McLean, Virginia

Sponsorship not specified$165k-$247kDetected 15 days ago
TypeScriptPythonJavaFull-Stack DevelopmentVector DatabasesAWSKubernetesCI/CDDevOpsRESTMachine LearningLLMsRAGAgentic AIMLOpsCommunicationCollaborationProblem SolvingAdaptability

About the role

  • Freddie Mac Enterprise Risk organization is seeking a hands-on Software Engineer Tech Lead (Gen AI) to lead the design and development of cutting-edge Generative AI (Gen AI) Agents, Agentic Workflows, RAG pipelines, and Gen AI Applications that solve complex business problems.
  • This role requires advanced proficiency in Python-based microservices for the orchestration layer deployed to AWS EKS (Kubernetes).
  • You will serve as a tech lead and a hands-on engineer, working alongside Gen AI scientists, product managers, and data engineers to shape and implement enterprise-grade Gen AI solutions.

Responsibilities

  • Design and implement scalable Full Stack Gen AI Agents, Agentic Workflows, RAG pipelines and applications to address diverse and complex business use cases.
  • Design and deploy Python-based microservices for robust orchestration and integration with Gen AI Large Language Models (LLMs).
  • Implement solutions leveraging modern design patterns and best practices for full stack development.
  • Build and maintain RESTful APIs to enable seamless communication between different system components.
  • Collaborate with cross-functional teams of full stack engineers, data engineers and Gen AI scientists to build full-stack Gen AI experiences.
  • Lead DevOps initiatives, including CI/CD pipelines, to ensure scalable and efficient deployment of Gen AI applications.
  • As a Software Engineer Tech Lead (Gen AI), your role is pivotal in shaping the future of AI-driven business solutions.

Requirements

  • 8-10 years overall software development experience
  • 2+ years hands-on experience in GenAI solutions, including experience with LLMs (OpenAI, Anthropic, AWS Bedrock, etc.)
  • 1+ year with agentic frameworks (Lang Graph, Lang Chain, etc.)
  • 5+ years in cloud development leveraging AWS, REST, microservices
  • Strong Python, Typescript, and Java experience
  • Demonstrated ability to work in cross-functional agile teams.
  • Experience with CI/CD practices and DevOps methodologies
  • Technical Proficiency: Demonstrate deep expertise in Python for creating microservices.

Nice to have

  • Bachelor's degree in computer science, Computer Engineering, IT or a related field.
  • Advanced studies/degree preferred.

Skills

  • Establish and enforce validation frameworks and procedures to ensure production-ready deployment of Gen AI solutions.

Compensation

  • This position has an annualized market-based salary range of $165,000 - $247,000 and is eligible to participate in the annual incentive program.

Benefits

  • 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.
  • Collaborate with Gen AI scientists to integrate machine learning models such as LLMs, RAG, and multi-modal AI into the application architecture.

Company info

  • 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.
  • At Freddie Mac, we are at the forefront of technological innovation, developing AI solutions that transform complex business challenges into streamlined, automated processes.
  • 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.

Equal opportunity

  • Please contact us to request accommodation.
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This listing is sourced directly from Freddie Mac's careers page and normalized into a canonical job model.