Advisor Software Engineer (AI/ML)

Fannie Mae•Reston, VA

16h•$155,000 - $209,000•Onsite

About The Position

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home. As a valued contributor to our team, you will design, produce, test, or implement software, technology, or processes across multiple projects, programs, or products, as well as create and maintain IT architecture, large scale data stores, and cloud-based systems. The Advisor Software Engineer (AI/ML) role will offer you the flexibility to make each day your own, while working alongside people who care so that you can determine the needs of the customer groups across multiple projects, programs, or products while identifying and resolving conflicting or complementary needs across customer groups. Design and develop software solutions to meet needs and may also lead matrixed teams. Apply extensive expertise in process-driven approach in designing solutions. Implement new software technology and coordinate simultaneous implementation tasks across teams. Oversee the maintenance of existing software. There is 1 opening for this position which can be based in our Reston, VA office. An Advisor role at Fannie Mae is on the same level as a Manager, but in an IC capacity.

Requirements

6 years of hands-on software engineering experience designing, developing, and maintaining scalable enterprise applications and cloud-native solutions.
Strong proficiency in Python development, including backend services, APIs, automation, data processing workflows, and production-ready AI/ML applications.
Strong skills in system design and architecture, including scalable, resilient, secure, and maintainable solution design.
Experience building API-driven solutions, including REST APIs, microservices, service orchestration, secure API development, and enterprise system integrations.
Hands-on experience with AWS cloud-native development, including serverless, event-driven, containerized, and distributed application patterns.
Experience with SQL and data platforms, including PostgreSQL, Snowflake, or similar relational and analytical database technologies.
Deep understanding of the software development lifecycle, including requirements analysis, design, development, testing, deployment, production support, and maintenance.
Experience with engineering best practices, including secure coding, code reviews, automated testing, CI/CD, observability, performance tuning, and production issue resolution.
Experience collaborating with technical and business stakeholders, including translating business needs into technical solutions and communicating risks, trade-offs, and delivery impacts.
Hands-on AWS software engineering experience, including application development using AWS service APIs, AWS CLI, AWS SDKs, and cloud-native deployment patterns.
Hands-on experience with core AWS services, including AWS Lambda, Amazon S3, Amazon EC2, Amazon API Gateway, IAM, CloudWatch, EventBridge, SQS, SNS, and Step Functions.
Experience with AWS AI/ML services, including Amazon SageMaker, Amazon Bedrock, and AWS-based model deployment or inference patterns.
Experience with containers and DevOps practices, including Docker, Kubernetes, ECS/EKS, CI/CD pipelines, automated testing, and release management.
Understanding of cloud security and compliance practices, including IAM, encryption, secrets management, vulnerability remediation, logging, and secure application design.
Hands-on experience in machine learning, AI engineering, data science, or applied AI solution development.
Hands-on experience with Generative AI and Large Language Models, including OpenAI, Anthropic, Cohere, Amazon Bedrock, or similar enterprise AI platforms.
Strong proficiency in Python and AI/ML libraries, including PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, and related ML frameworks.
Experience building Retrieval-Augmented Generation solutions, including embeddings, vector databases, semantic search, document retrieval, chunking strategies, prompt grounding, and response evaluation.
Experience with LLM application patterns, including prompt engineering, guardrails, model evaluation, tool/function calling, agentic workflows, and responsible AI considerations.
Familiarity with AI application frameworks and tools, such as LangChain, LlamaIndex, FastAPI, MCP tools, vector databases, and API-based AI service integration.
Experience with model development and deployment practices, including feature engineering, model serving, model monitoring, MLOps, and productionizing AI/ML capabilities.
Proven experience leading technical delivery within software engineering teams, including solution direction, task assignment, progress monitoring, issue resolution, and delivery accountability.
Experience mentoring and coaching engineers, including technical guidance, code review feedback, design support, and professional development.
Ability to influence technical decisions and engineering practices, including architecture discussions, design trade-offs, quality improvements, and adoption of modern AI/ML and cloud engineering standards.
Experience partnering with product owners, architects, business stakeholders, risk, security, and operations teams to deliver solutions aligned with business outcomes and enterprise standards.
Ability to produce high-quality technical documentation, including architecture papers, solution design documents, technical white papers, AI/ML implementation guides, and executive-ready technical summaries.
Experience contributing to innovation artifacts, such as invention disclosures, patent-supporting technical writeups, proof-of-concept documentation, and publication-ready technical papers when applicable.

Nice To Haves

Bachelor’s or master’s degree in Computer Science, Engineering, Information Technology, Data Science, Machine Learning, Artificial Intelligence, or a related field.
Experience designing and delivering AI-enabled enterprise software solutions, including GenAI applications, intelligent automation, AI-assisted workflows, and AI-driven decision support.
Experience with MLOps, vector databases, embedding-based search, MCP-based tool integration, and enterprise AI governance practices.
Experience writing technical papers, invention disclosures, patent-supporting documentation, or reusable engineering playbooks for emerging technology solutions.
Experience with testing strategies and tools, including unit, integration, functional, regression, and performance testing.
Experience with Scaled Agile Framework, Agile methodology, cybersecurity vulnerability remediation, and enterprise delivery practices.
Strong relationship management skills with the ability to collaborate across stakeholders, influence outcomes, and support strategic enterprise technology initiatives.

Responsibilities

Determine the needs of the customer groups across multiple projects, programs, or products while identifying and resolving conflicting or complementary needs across customer groups.
Design and develop software solutions to meet needs and may also lead matrixed teams.
Apply extensive expertise in process-driven approach in designing solutions.
Implement new software technology and coordinate simultaneous implementation tasks across teams.
Oversee the maintenance of existing software.