AI Full-Stack Engineer

We are looking for an AI Full-Stack Engineer to build and deliver end-to-end software products that integrate modern AI capabilities into real operational workflows. This role spans frontend, backend, and AI application development, with a strong emphasis on using agentic coding tools to accelerate delivery and improve engineering throughput. You will work on production-grade applications that combine intuitive user experiences with AI-powered automation, decision support, and knowledge workflows. LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors-helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. Responsibilities:

• Design, build, and ship full-stack applications from user interface to backend services
• Develop AI-enabled features using LLMs, retrieval-augmented generation (RAG), tool-use agents, and orchestration frameworks
• Integrate AI capabilities into existing enterprise platforms, workflows, and data systems
• Build secure, scalable APIs and services to support AI-powered applications
• Collaborate with product, platform, and security teams to deliver compliant, mission-ready solutions
• Use agentic coding workflows (e.g., Anthropic Claude Code, Google Gemini Code Assist, OpenAI Codex, xAI Grok Code Fast, etc.) to accelerate implementation, testing, and iteration
• Contribute to engineering standards for AI quality, evaluation, observability, and responsible deployment, What Success Looks Like
• Shipping production-ready AI-enabled features quickly and safely
• Building software that is usable, maintainable, and operationally scalable
• Leveraging agentic development workflows to increase delivery velocity
• Partnering effectively across product, engineering, and compliance teams
• Helping establish best practices for AI-native software delivery

Why This Role Matters AI is changing how software is built and operated. This role is for engineers who want to be at the center of that shift - delivering full-stack applications where AI is not an add-on, but a core capability The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances. Applicants must meet eligibility requirements for a U.S. Government security clearance. Only US Citizens are eligible for a security clearance. For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work. Job Locations: US-VA-Tysons

• Strong experience building and delivering modern full-stack applications
• Proficiency with backend development (Node.js, Python, Java, or similar) and API design
• Frontend experience with React, Next.js, or comparable frameworks
• Practical experience integrating AI/LLM capabilities into software products
• Familiarity with RAG pipelines, vector databases, prompt engineering, and agent toolchains
• Experience working in cloud environments (AWS, Azure, or GCP) with containerized deployments
• Strong engineering fundamentals: testing, version control, CI/CD, and operational reliability, * Experience deploying AI systems in regulated or enterprise environments
• Familiarity with model evaluation, guardrails, and AI assurance practices
• Exposure to DevSecOps, compliance automation, or secure platform engineering
• Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or custom agent runtimes
• Knowledge of FedRAMP, NIST, or other compliance-driven delivery constraints