Software Engineer, Agentic Modeling & Simulation (Systems Engineer, Sr)
Redwire Software Engineer, Agentic Modeling & Simulation (Systems Engineer, Sr)
- Develop agentic system capabilities — Build and integrate AI agents, autonomous workflows, and LLM‑driven decision systems into backend architectures.
- Design high‑performance backend services — Implement low‑latency, high‑throughput services in Python, C++, or Rust.
- Architect real‑time processing pipelines — Build deterministic, concurrent, or multi‑threaded pipelines for real‑time agentic decision loops.
- Develop and govern data‑access layers — Implement indexing, query optimization, and data‑model governance for evolving knowledge domains.
- Build and optimize APIs — Design REST, GraphQL, and gRPC interfaces with strong schema governance and versioning.
- Integrate graph‑centric data systems — Model agent memory, context graphs, and reasoning structures using graph databases.
- Ensure reliability and observability — Implement logging, metrics, tracing, error handling, and automated testing.
- Collaborate across engineering domains — Work with systems engineers, simulation experts, analysts, and DevOps to define clean integration boundaries.
- Support secure and compliant operations — Apply authentication, authorization, secrets management, and secure‑by‑design principles.
- STEM foundation — Bachelor’s degree in CS, Engineering, Mathematics, or related field, or equivalent experience.
- Backend & systems engineering — 1–5 years building backend systems, distributed services, or data‑driven pipelines.
- High‑performance programming — Proficiency in Python, C++, or Rust for low‑latency or high‑throughput systems.
- Agentic system integration — Experience integrating AI agents, autonomous workflows, or LLM‑based decision systems.
- Graph‑centric data modeling — Experience with Neo4j, DGraph, ArangoDB, or similar technologies.
- Database schema & modeling — Experience with relational, graph, and document databases.
- Real‑time processing — Experience with concurrent, deterministic, or multi‑threaded pipelines.
- High‑throughput data APIs — Experience with streaming systems and binary transport formats.
- Networking & data transport — Expertise with UDP/TCP, Pub/Sub, and distributed messaging.
- GPU‑accelerated computation — Understanding of CUDA, GPU kernels, or heterogeneous compute architectures.
- API design expertise — Experience designing REST, GraphQL, and gRPC APIs.
- Microservice architectures — Familiarity with containerized deployments and service‑to‑service patterns.
- CI/CD integration — Experience integrating backend services into CI/CD pipelines.
- Service reliability fundamentals — Observability, error handling, contract validation, and automated testing.
- API & data security — Strong understanding of authentication, authorization, and secure data‑access patterns.
- Engineering rigor — Experience working in aerospace/defense or other high‑integrity environments.
- Security eligibility — U.S. Citizen; able to obtain and maintain a DoD Secret clearance (TS/SCI preferred).
- Multi‑protocol API development — REST, gRPC, SOAP, GraphQL.
- Agent‑oriented data structures — Modeling agent memory, context graphs, or reasoning chains.
- HPC‑adjacent workflows — Simulation data, scientific computation, or data‑dense analytics.
- Simulation & modeling systems — Integrating AI agents with simulation engines or digital‑engineering tools.
- Distributed computation frameworks — Job orchestration, distributed compute, or Monte Carlo automation.
- High‑rate data processing — Optimizing ingestion and processing for high‑rate sensor or telemetry data.
- Regulated industry exposure — Aerospace, defense, robotics, or similar domains.
- Internal tooling development — Tools or libraries used across engineering teams.
- Cross‑functional collaboration — Work with systems engineers, analysts, simulation experts, and product teams.
- Open‑source contributions — Contributions to backend frameworks, agent libraries, or data‑modeling tools.