The
AI/ML Engineer designs, builds, and scales the backend, data, and computational infrastructure that powers intelligent agents, autonomous workflows, and AI-driven decision systems. This role blends high-performance backend engineering, graph-centric data modeling, real-time processing, and secure API design with emerging agentic architectures. You will work across simulation, data, and systems engineering teams to deliver intelligent, reliable, and mission-aligned agentic capabilities supporting aerospace, defense, and other high-integrity environments.
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).
Desired Skills- 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.
