About Adelphi
Adelphi's Vision is to build the biggest AI Deployment company for National Security, which requires talent that is extremely good at problem solving, gets excited about National Security challenges, and thrives in software factories. We're executing a Mission that (1) Identifies the core bottlenecks in Warfighting, Intelligence and the Enterprise, (2) Assigns top talent with backgrounds in systems and machine learning engineering, and (3) Finds repeatability in solution development to target similar offices across the DoW/IC. The business has 10x'd in under two years and has built trust with the senior most leaders at the Department of War and Intelligence Community.

About the Role: As Senior Software Engineer, you will be making technical direction and architectural decisions for the platform and writing a large share of its code. Our core is Rust: a single binary that serves as the AI/API gateway, policy engine, and Kubernetes controller, and a separate Rust ingestion worker that runs in Customer environments. Around it sit Helm charts and Argo CD for a GovCloud EKS cluster, Terraform for the infrastructure underneath, and Python internal tools. This role requires a mix of Rust application development and DevOps/Kubernetes practice. Expect the split to be roughly 70/30 Rust to infrastructure. The right candidate will bring deep experience building, shipping, and supporting mission-critical services and can be productive in this codebase within weeks, not months. Experience in the national security field is a strong plus to best understand Customer requirements and how that will drive the product roadmap. We are an AI-native engineering team. We expect every engineer to leverage LLMs and AI tooling as a core part of how you design, build, ship, and debug software as an integral, daily extension of your workflow.

Clearance Requirement: An active U.S. Government Secret clearance is not required. Candidates must be U.S. citizens and be eligible to maintain and upgrade clearances as the role requires. Candidates need to be excited to work for the Defense and Intel Communities, and to apply AI to real applications for Government.

Is this you?

We are an AI-native engineering team. We expect every engineer to leverage LLMs and AI tooling as a core part of how they design, build, ship, and operate agentic systems that turn frontier-model capability into mission outcomes.

• Technical Leadership: You have 8+ years of experience with meaningful time at Senior level or above. You own architecture for mission-critical production systems, think in systems and trade-offs, and thrive in ambiguous environments with high ownership.

• Domain & Tech Stack: You are proficient in Rust and have shipped production services in it (async/tokio, hyper or axum, serde, and a strong instinct for ownership and error handling). You are also proficient with Python (FastAPI, Flask, or Django) and comfortable owning the tooling around a service. Kubernetes experience (Helm, Argo CD, EKS) and AWS are strongly preferred; Terraform is a plus. Experience handling and securing large-scale production data, and experience in government or national security environments, are strong pluses.

• AI-Native by Default: You use AI coding tools daily and instinctively, prompt-engineer through complex architecture and debug sessions, and maintain a clear-eyed view of AI limitations in high-security contexts. You know when to trust AI-generated output and when to verify.

• Mission-Driven: You are excited by how AI enables better solutions to hard, long-standing problems like data sharing, and you want to see that work deployed in environments where it matters.

Bonus Points:

• Security engineering: FIPS cryptography, mTLS and SPIFFE workload identity, policy engines, or RMF/ATO compliance work.

• SRE experience: on-call ownership, observability, and incident response for production Kubernetes services.

• Infrastructure as code with Terraform and GitOps, including air-gapped or on-premise deployments.

• Production experience launching agentic services leveraging LLMs, embeddings, and inference.