Software Engineer, AI & Application Engineering (Contingent)

Coforma•North Bethesda, MD

3d•$150,236 - $169,147•Remote

About The Position

We are looking for an experienced and detail-oriented Software Engineer to design, build, and support intelligent, modern applications for a federal government partner at Coforma. This opening is contingent upon Coforma receiving the award for the underlying contract associated with this project. Our Software Engineers enjoy tackling complex problems while working with other members of our cross-disciplinary teams to deliver elegant products across a modern technology stack. They are skilled across the stack, building accessible applications that put people first with code that is clean, maintainable, well-documented, and well-tested. This role combines full-stack web application engineering with hands-on AI and machine learning development, enabling you to deliver both the user-facing systems that serve federal employees and retirees and the AI-powered capabilities that make those systems smarter and more efficient over time. Join us to build software that makes an impact and implement human-centered solutions that we can be proud to say we worked on together. If you’re passionate about improving lives through digital products and services, Coforma is a great company for you.

Requirements

Full-time resident of the contiguous United States (must be legally authorized to work in the US now and in the future without sponsorship)
Ability to pass a Public Trust background check and any applicable background and/or security checks related to project requirements
Proficiency in React, Vue.js, JavaScript, TypeScript, ASP.NET Core, and Java (Spring Framework)
Strong SQL skills and experience with SQL Server as well as familiarity with EF Core
Experience with enterprise integrations including message queues, database RPC, REST, and SOAP APIs
Understanding of authentication, authorization, and security best practices
Hands-on experience building or deploying LLM-based solutions (e.g., OpenAI, Anthropic, Azure OpenAI, or open-source models)
Familiarity with agentic AI patterns (tool use, planning, memory, and multi-step reasoning)
Experience with RAG pipelines, vector databases, and embedding-based retrieval
Experience using coding agents such as CoPilot or Claude Code
Effective prompt engineering and experience evaluating LLM outputs for reliability and correctness

Nice To Haves

5+ years of experience building applications with open-source programming languages
5+ years of experience working with modern frameworks, libraries, or runtimes
5+ years of experience working with APIs and databases of various types
5+ years of experience working with infrastructure tools and Infrastructure as Code
Experience developing software in a remote environment
Experience working in the digital services, federal government, or federal government contracting industries

Responsibilities

Build modular, responsive web applications using React and/or Vue.js in alignment with accessibility and UX best practices.
Design and implement secure, scalable APIs using TypeScript, ASP.NET Core, or Java (Spring Framework).
Integrate with enterprise systems via SOAP/RESTful APIs with robust error handling.
Implement authentication, authorization, and secure configuration in accordance with client agency security policies.
Optimize database access and performance using SQL Server and Entity Framework Core.
Add observability through structured logging, metrics, and tracing and support incident response with clear runbooks.
Design and implement AI agents and large language model (LLM)-powered applications that automate and enhance retirement data workflows.
Build, evaluate, and maintain machine learning models for classification, prediction, and anomaly detection.
Integrate LLMs into production systems using prompt engineering, retrieval-augmented generation (RAG), and agentic frameworks.
Develop and maintain data pipelines that ingest, validate, and transform large-scale structured and unstructured data.
Apply explainable AI practices appropriate for a regulated, auditable government environment.
Track model performance and manage the machine learning (ML) lifecycle using tools such as MLflow.
Participate in Agile ceremonies, conduct code reviews, and contribute to release planning and documentation.
Work across technical and policy teams to translate business requirements into reliable, AI-driven solutions.
Uphold software engineering best practices such as version control, CI/CD, testing, and peer review across both application and AI workstreams.