AI Software Engineer

Responsibilities

• Software Development and Integration
• Design, develop, and deploy AI-powered automation tools, workflows, and integrations that serve cross-functional business needs.
• Build and maintain APIs, microservices, and data pipelines that connect AI capabilities to business applications and platforms.
• Implement and configure commercial AI/automation platforms where build vs. buy decisions favor adoption over custom development.
• Write clean, well-documented, testable code following established engineering standards and best practices.
• AI and Automation Implementation
• Develop and fine-tune AI models, prompts, and automation workflows to address specific operational bottlenecks identified by business units.
• Integrate large language models, machine learning frameworks, and AI services into enterprise applications and workflows.
• Build internal tools and interfaces that enable non-technical teams to leverage AI capabilities effectively.
• Continuously evaluate and incorporate emerging AI tools and techniques to improve solution quality and delivery speed.
• Security-First Development
• Embed security principles into all phases of the software development lifecycle, including secure coding practices, input validation, authentication, and authorization controls.
• Conduct code reviews with a focus on identifying and remediating security vulnerabilities.
• Ensure all AI integrations, API endpoints, and data flows are designed with least-privilege access, encryption in transit and at rest, and proper logging and monitoring.
• Collaborate with cloud security and compliance teams to validate that deployments meet CMMC, NIST SP 800 series, DFARS, and other applicable regulatory requirements.
• Support secure deployment practices including infrastructure-as-code, container security, and CI/CD pipeline hardening.
• Platform and Infrastructure Support
• Deploy and manage solutions across cloud and hybrid environments (Azure, AWS, or GCP) following organizational security and architectural standards.
• Monitor application performance, reliability, and security posture in production environments.
• Troubleshoot and resolve technical issues across the full stack, from data layer to user-facing applications.
• Collaboration and Documentation
• Partner with the AI Solutions Architect to refine solution designs and ensure technical feasibility.
• Participate in business unit meetings to understand requirements, demonstrate progress, and gather feedback.
• Maintain comprehensive technical documentation including architecture diagrams, API specifications, runbooks, and deployment procedures.
• Contribute to the development of reusable components, libraries, and patterns that accelerate future solution delivery.

Requirements

• US Citizenship Required Must possess and be able to maintain a Secret Clearance.
• Bachelor's degree in Computer Science, Software Engineering, or a related discipline.
• A minimum of four years of progressive experience in software engineering, with at least two years of hands-on experience building AI/ML-powered applications or automation solutions.
• Strong proficiency in modern programming languages such as Python, TypeScript/JavaScript, or Go.
• Demonstrated experience building and deploying APIs, microservices, and cloud-native applications.
• Practical experience with AI/ML frameworks, large language model integration, or automation platform development.
• Familiarity with secure software development practices and experience operating in regulated environments with compliance frameworks such as CMMC, NIST SP 800 series, DFARS, or equivalent.
• Experience with cloud platforms (Azure, AWS, or GCP) and infrastructure-as-code tools.
• Five or more years of software engineering experience with a focus on AI/automation.
• Experience in the government contracting or defense sector.
• Hands-on experience with LLM APIs, prompt engineering, RAG architectures, and AI agent frameworks.
• Experience with container orchestration (Kubernetes, Docker), CI/CD pipelines, and DevSecOps practices.
• Familiarity with data engineering concepts including ETL pipelines, data warehousing, and streaming architectures.
• Relevant certifications in cloud platforms, AI/ML,
• Minimum Qualifications

Additional Information

The AI Software Engineer is responsible for building, integrating, and maintaining AI-driven automation solutions that improve operational efficiency across multiple business units. Working closely with the AI Solutions Architect, this role translates solution designs into production-ready software, APIs, and platform integrations. The AI Software Engineer operates with a security-first mindset, ensuring all code, integrations, and deployments meet the organization's security standards and regulatory obligations. This role is hands-on and delivery-focused, requiring strong software engineering fundamentals combined with practical experience in AI/ML tooling and modern cloud platforms.

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