Senior AI Software Developer

Overview:

Pyramid Systems is seeking a Senior AI Software Developer to build AI-enabled applications and lead the modernization of legacy federal systems—including COBOL-to-Python re-platforming—for our HUD customer and the AIR product platform. This is an applied AI engineering role focused on delivering production software that leverages modern AI services (LLMs, APIs, and cloud AI platforms), not building foundation models. You will work directly with HUD stakeholders and the NEXUS/AIR team to ship secure, compliant, mission-aligned capabilities.

Responsibilities:

Serve as a senior technical leader on AI-enabled application work — own end-to-end architecture, set technical direction across multiple projects, and represent Pyramid Systems in technical conversations with HUD and other federal stakeholders.

Design, develop, and deploy AI-enabled applications that integrate LLMs, generative AI services, and cloud AI APIs into production federal systems

Build full-stack web applications end to end — modern front-end interfaces (React, Angular, or Vue with TypeScript), back-end services (Python with Django/Flask/FastAPI or Node.js), and the database, API, and integration layers behind them

Design and implement secure CI/CD pipelines and DevSecOps practices — automated build, test, and deploy; SAST/DAST and dependency scanning; container and image scanning; secrets management; SBOM generation; and shift-left security gates

Author infrastructure as code (Terraform, CloudFormation, or equivalent) and manage containerized workloads (Docker, Kubernetes) on FedRAMP-authorized cloud environments

Implement logging, monitoring, tracing, and alerting (CloudWatch, Datadog, OpenTelemetry, or similar) so AI-enabled applications are observable and operable in production

Drive engineering quality through automated testing (unit, integration, end-to-end), Git-based code review, and trunk-based or GitHub-flow branching practices

Support Agile delivery (Scrum or Kanban) including sprint planning, backlog refinement, and demos with federal product owners

Lead modernization of legacy federal applications, including translating and re-platforming COBOL (and other legacy languages) to Python and modern cloud-native architectures

Partner with HUD stakeholders, product, and engineering teams to translate mission needs into secure, compliant, AI-enabled software solutions

Build and maintain APIs, microservices, and integration layers that embed AI capabilities into customer-facing and back-office applications

Apply the NIST AI Risk Management Framework (AI RMF 1.0) and related federal guidance (e.g., OMB M-24-10, EO 14110) to ensure trustworthy, responsible AI use

Implement responsible-AI practices including evaluation, monitoring, prompt and output safety, bias mitigation, explainability, and human-in-the-loop controls

Ensure compliance with federal security and privacy requirements (FISMA, FedRAMP, NIST 800-53, Section 508) across all AI-enabled deliverables

Contribute to the AIR platform (including AIR-Quire and AIR Grant) by building demos, prototypes, and reusable components that accelerate client engagements

Mentor and coach mid-level developers, lead code reviews, and drive engineering best practices for AI-enabled application development

Support proposal efforts and client demonstrations as named or contributing technical personnel, including for the HUD recompete (TO1, TO2, FCAT)

Research and evaluate emerging AI tools, frameworks, and patterns (RAG, agentic workflows, model orchestration) and pilot their use in federal contexts

Work with legacy teams to identify and leverage appropriate AI tools and implementations to increase developer velocity, improve quality, and modernize legacy systems.

Support culture changes surrounding AI use at Federal Agencies including government stakeholders and internal development teams.

Help drive organizational and cultural adoption of AI capabilities across project teams by addressing concerns, increasing understanding, and demonstrating practical mission-focused value.

Collaborate with leadership to identify modernization opportunities that improve operational efficiency, reduce technical debt, and support Agency mission objectives.

Communicate AI modernization strategies and benefits to technical and non-technical stakeholders, including articulating how AI can enhance mission delivery, improve workforce efficiency, and augment existing teams.

Qualifications:

US Citizenship is required

Bachelor's or master's degree in Computer Science, Software Engineering, or a related field

8+ years of software development experience, with at least 4+ years building AI-enabled applications (LLM integration, generative AI, or applied AI/ML in production)

Strong programming skills in Python; experience with at least one legacy language (COBOL, mainframe languages, or similar) and a demonstrated ability to modernize legacy code

Hands-on experience integrating commercial and open-source AI services and APIs (e.g., OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI)

Hands-on experience with cloud platforms (Azure, AWS, or Google Cloud), including deploying production applications

Working knowledge of the NIST AI Risk Management Framework (AI RMF 1.0) and federal AI policy (OMB M-24-10, EO 14110)

Experience with REST APIs, microservices architecture, and modern application design patterns

Strong understanding of secure software development practices in federal environments (FISMA, FedRAMP, NIST 800-53)

Demonstrated experience as a technical lead, principal engineer, or senior architect — including leading small teams, owning architecture for production systems, and mentoring other engineers

Excellent communication skills and ability to engage directly with federal customers

Preferred Qualifications

Experience with generative AI, large language models (LLMs), and NLP applications

Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow)

Knowledge of containerization tools (Docker, Kubernetes)

Experience working with big data technologies (e.g., Spark, Hadoop)

PhD or advanced specialization in AI/ML is a plus