AI Software Engineer - Remote

ISI Enterprises•Reston, VA

2d•$140,000 - $170,000•Remote

About The Position

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.

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.

Nice To Haves

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, or security are desirable.

Responsibilities

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.
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.
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.
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.
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.