AI Software Engineer

Second Front Systems•Washington, DC

About The Position

Second Front Systems is seeking a mission-focused, full-stack AI Software Engineer to join its Research and Development organization and build AI-native platform capabilities. The role focuses on designing, building, and shipping production agentic AI systems, along with establishing the cloud infrastructure and governance those systems require. The engineer will take AI systems from prototype to production across agent design, implementation, LLM integration, and cloud deployment, with attention to output quality, traceability, and user authorization. The role includes building agentic systems with frameworks such as LangGraph and LangChain, connecting agents to tools and knowledge, deploying cloud infrastructure, and defining evaluation suites to guide model selection and architecture decisions. The engineer will partner with Product and Engineering teams to identify and deploy AI workflows across onboarding, deployment, monitoring, and remediation; advise on architecture, scope, model selection, and responsible AI governance; and create documentation and reusable components that expand organizational AI capabilities. Candidates must be U.S. citizens and reside in an approved hiring hub: DC/Maryland/Virginia; Raleigh/Durham/Chapel Hill, North Carolina; Denver/Colorado Springs, Colorado; or Dallas/Fort Worth, Texas. The position is full-time. The base salary range is $150,000–$160,000, with potential eligibility for equity awards and discretionary bonuses. Flexible remote work opportunities are offered.

Requirements

At least 2 years of full-time software, ML, or AI engineering experience, including 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).
Ability to communicate AI concepts and recommendations to non-technical stakeholders and cross-functional teams.
U.S. citizenship.
Residence in an approved hiring hub: DC/Maryland/Virginia; Raleigh/Durham/Chapel Hill, North Carolina; Denver/Colorado Springs, Colorado; or Dallas/Fort Worth, Texas.

Nice To Haves

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 such as LiteLLM, vLLM, or GPU-backed self-hosted models, including single- and multi-tenant designs.
Experience driving AI adoption across a product organization and enabling others to use AI effectively.
Experience working in or with the Department of Defense, federal agencies, or regulated environments.
Familiarity with compliance frameworks such as NIST 800-53, FedRAMP, or NATO D32.

Responsibilities

Architect production agentic AI systems using LangGraph and LangChain, emphasizing reliability, traceability, and measurable output quality.
Build agent components, including ingestion pipelines, embedding strategies, chunking, retrieval, re-ranking, and tool suites integrated with managed knowledge-base services, vector databases, and internal and external APIs.
Connect agents to corporate tools and knowledge through Model Context Protocol by building MCP servers and consuming clients.
Establish and operate cloud infrastructure for R&D AI systems using 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, and run these suites in CI.
Partner with Product teams to identify and scope high-leverage AI opportunities across workflows and advise on agent design, model selection, and output quality standards.
Build documentation and reusable components that strengthen organizational AI capabilities.
Advise on responsible AI governance and translate research-grade AI systems into mission-ready products.

Benefits

Competitive salary.
100% employer-paid medical, dental, and vision coverage for the employee and dependents.
401(k) with a 3% company contribution.
Wellness benefits, including a One Medical membership, mental health resources, and family planning support.
Equity incentive plan and potential eligibility for equity awards.
New-hire technology and home office stipend.
Annual professional development stipend.
Flexible paid time off and all federal holidays.
Generous parental leave.
Flexible remote work opportunities.
Employee referral bonus program.
Potential eligibility for discretionary bonuses.