AI Software Engineer

AI Software Engineer

Second Front Systems is looking for an ambitious, mission-focused, full-stack AI Software Engineer to strengthen our growing Research and Development organization building AI-native platform capabilities.

Operating at the intersection of AI systems engineering and national security infrastructure, you will be responsible for designing, building, and shipping production agentic AI systems while establishing the cloud infrastructure and governance they operate within.

You will move from prototype to production across agent design, implementation, LLM integration, and cloud deployment, ensuring measurable output quality and user authorization at every stage, ensuring that Game Warden and downstream products integrate AI as foundational capability rather than feature. Your solutions will include evaluative rigor and organizational fluency.

We need someone capable of building and shipping agentic systems with LangGraph, LangChain, tool-calling, and multi-step orchestration; operating cloud infrastructure (EKS, Terraform, Helm) and LLM gateway patterns across AWS Bedrock, Anthropic, OpenAI, Cohere, self-hosted models and provider-agnostic architectures; and defining and measuring agent quality through evaluation suites, then translating results into model selection and architecture decisions.

As a key partner to our Product and Engineering teams, which are seeking to identify and deploy AI workflows across onboarding, deployment, monitoring, and remediation, you will advise on agent architecture and scope, model selection, implementation, and responsible AI governance. You will build documentation and reusable components that multiply organizational AI capability, serving as technical authority translating research-grade AI systems into mission-ready product.

Note: Candidates must reside in one of our approved hiring hubs:

• DC/Maryland/Virginia
• Raleigh/Durham/Chapel Hill, NC
• Denver/Colorado Springs, CO
• Dallas/Fort Worth, TX

This is a full-time position.

What You’ll Do

• Architect production agentic AI systems using LangGraph and LangChain with focus on reliability, traceability, and measurable output quality.
• Build agent components including ingestion pipelines, embedding strategies, chunking, retrieval, and re-ranking, and tool suites across managed knowledge-base services, vector databases, and internal and external APIs.
• Connect agents to corporate tools and knowledge through Model Context Protocol, constructing both MCP servers and consuming clients.
• Establish and operate cloud infrastructure for R&D AI systems: Amazon EKS, Terraform, Helm, and GitOps delivery patterns.
• Measure agent and LLM output quality through evaluation and regression suites covering groundedness, faithfulness, relevance, and retrieval quality, running in CI.
• Partner with Product teams to scope high-leverage AI opportunities across workflows and advise on agent design, model selection and output quality standards.

What You Bring

• 2+ years full-time software, ML, or AI engineering experience with hands-on production work building and shipping LLM-based systems.
• Hands-on experience building agentic AI systems with tool-calling agents and multi-step orchestration using LangGraph, LangChain, or equivalent.
• Production experience with Retrieval-Augmented Generation including vector stores, embedding models, chunking strategies, and retrieval quality optimization.
• Experience deploying containerized AI services to cloud environments using Docker, Kubernetes (EKS preferred), and infrastructure as code (Terraform preferred).
• Demonstrated ability to communicate AI concepts and recommendations to non-technical stakeholders and cross-functional teams.
• U.S. citizenship and residence in approved hiring hub: DC/Maryland/Virginia, Raleigh/Durham/Chapel Hill NC, Denver/Colorado Springs CO, or Dallas/Fort Worth TX.

Preferred

• Experience building MCP servers or clients, or integrating agents with enterprise tools and knowledge bases.
• Experience with AWS AI services such as Bedrock Knowledge Bases, AgentCore, and OpenSearch.
• Experience operating LLM gateways or serving infrastructure (e.g., LiteLLM, vLLM, GPU-backed self-hosted models), including single- and multi-tenant designs.
• Experience driving AI adoption across a product organization: not just building, but enabling others to use AI effectively.
• Experience working in or with the…