Software Engineer, Technical Lead (Gen AI Engineering)

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:


We are seeking a highly experienced hands-on Software Engineer, Technical Lead (Gen AI) to lead the design and development of cutting-edge Generative AI (Gen AI) Agents, Agentic Workflows, and Gen AI Applications that solve complex business problems in multi-family business. This role requires advanced expertise in Retrieval-Augmented Generation (RAG), vector databases and Python based frameworks like LangChain. The ideal candidate will have a strong foundation in data pipelines, logging and observability practices, ensuring production grade reliability and scalability for AI-driven applications. You will work in a hands-on engineer role, working alongside Gen AI experts, product managers, and data engineers to shape and implement production grade Gen AI solutions to solve our complex business problems.



Our Impact:


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 business solutions ensures delivery of impactful and sustainable results for our clients.



Your Impact:


As Software Engineer, Technical Lead (Gen AI), your role is pivotal in shaping the future of AI-driven business solutions within Multi-Family. You will have the opportunity to design and develop scalable applications end-to-end that integrate sophisticated AI models, directly influencing how our business operates and succeeds.



Qualifications:

• Bachelor's degree in computer science, Computer Engineering, IT or a related field. Advanced studies/degree preferred.
• 8-10 years of experience in software development
• 5+ years of experience in applied AI/ML engineering
• Strong proficiency in Python and experience with LangChain or similar framework
• Experience in data transformation, cleansing, and feature engineering to prepare data for AI/ML models
• Knowledge of implementing APIs, microservices and distributed systems within cloud environment. Familiarity with AWS services is expected
• Strong programming skills and familiarity with AI/ML libraries and frameworks.
• Solid understanding of MLOps/DataOps pipelines for scalable AI/ML development
• Experience with logging, tracing and observability frameworks
• Exposure to LLM fine-tuning, prompt engineering and model evaluation is must
• Experience with managed LLM platforms such as AWS Bedrock, Azure OpenAI or GCP Vertex AI is a plus
• Demonstrated ability to work in cross-functional agile teams with a proven ability to collaborate with data engineers to ensure data readiness and quality for Gen AI solutions.

Key Responsibilities:

• Design and implement scalable Full Stack Gen AI Agents, Agentic Workflows, and applications to address diverse and complex business use cases.
• Design and implement feature engineering workflows to extract relevant signals from structured and unstructured data, enhancing the performance of LLM-powered products.
• Ensure that data used by AI applications is clean, well-structured, and optimized for model consumption.
• Establish feedback mechanisms where outputs from LLM models are analyzed and used to enrich and improve the underlying data sources, creating a virtuous cycle for product improvement.
• Design and deploy Python-based microservices for robust orchestration and integration with Gen AI Large Language Models (LLMs).
• Integrate machine learning models such as LLMs, RAG, and multi-modal AI into the application architecture.
• 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 experts to build full-stack Gen AI experiences.
• Lead DevOps initiatives, including CI/CD pipelines, to ensure scalable and efficient deployment of Gen AI applications.
• Stay updated with advances in LLMs, vector search, prompt engineering and retrieval strategies to continuously improve system capabilities.

Keys to Success in this Role:

• Quality: Delivery of robust, fault tolerant RAG pipelines with high availability and performance
• Technical Proficiency: Demonstrate deep expertise in Python for creating microservices.
• Data Readiness: Ensure high-quality, well-prepared data is available for Gen AI model training and inference.
• Operational excellence: Strong monitoring and observability practices leading to minimal downtime and quick issue resolution
• Cross-team impact: Work effectively with Gen AI experts, UX designers, product managers, and data engineers to build comprehensive Gen AI solutions. Strong communication skills and a team-oriented mindset will be key.
• Continuous learning and innovation: Stay in touch with industry trends and proactively assess and recommend the best patterns/practices to improve the Gen-AI apps
• Agility and Adaptability: Thrive in a fast-paced, agile environment. Your flexibility and ability to adapt to new challenges and technologies will ensure the continuous improvement of our AI solutions.

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 $154,000 - $230,000 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.