Software Engineer, Tech Lead (Gen AI Engineering)

Freddie Mac•Mclean, VA

53d•$154,000 - $230,000

About The Position

We are seeking a highly experienced hands-on Software Engineer, Tech 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 an Agile Development Tech 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.

Requirements

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
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.

Nice To Haves

Experience with managed LLM platforms such as AWS Bedrock, Azure OpenAI or GCP Vertex AI is a plus

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.