AI/ML Engineer (Systems Engineer, Sr)

Major Responsibilities

• 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.

Ideal Experience

• 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).