AI Infrastructure Engineer – Agents & ML Systems

Job Responsibilities

• Build internal AI infrastructure that connects LLMs and AI agents with internal tools, APIs, data sources, data lakes, telemetry stores, simulation tools, code repositories, documentation systems, logs, and engineering workflows.
• Develop and maintain agentic AI systems for task automation, data analysis, engineering support, simulation workflows, and internal productivity.
• Build tool integration and connector infrastructure for AI agents, including MCP and other emerging tool-use standards, spanning servers, tools, resources, prompts, connectors, and secure tool-use patterns.
• Create pipelines for retrieval, RAG, context management, document processing, embeddings, and internal knowledge search.
• Support ML infrastructure workflows such as data preparation, dataset curation, experiment tracking, model evaluation, fine-tuning support, and model deployment.
• Build evaluation frameworks for agent performance, tool-use reliability, task success, model quality, regression testing, and failure analysis.
• Develop observability, logging, tracing, auditability, monitoring, and debugging tools for AI agents, model calls, MCP tools, and ML pipelines.
• Partner with Autonomy, Software, Data, Simulation, Product, and Operations teams to identify high-value AI use cases and turn them into reliable internal tools.
• Secure agentic AI systems end-to-end with least-privilege tool access, sandboxed tool execution, prompt-injection and misuse mitigation, secrets management, human-in-the-loop approvals, and safe handling of sensitive and defense data.
• Maintain documentation, reusable examples, templates, and best practices that help internal teams adopt AI tools safely and effectively.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Applied Mathematics, or a related technical field.
• 3+ years of experience in software engineering, infrastructure engineering, ML infrastructure, backend systems, data engineering, developer tools, or related technical roles.
• Strong programming experience in Python, TypeScript, Go, C++, or similar languages.
• Experience building production software systems, APIs, services, data pipelines, or internal platforms.
• Experience with, or strong interest in, LLM applications, AI agents, tool-using systems, RAG pipelines, or AI developer tools.
• Familiarity with modern AI infrastructure concepts such as embeddings, vector search, prompt management, evaluation, model serving, fine-tuning, or MLOps.
• Ability to integrate systems across APIs, databases, object stores, documents, logs, internal tools, and structured or unstructured data sources.
• Strong understanding of production engineering fundamentals, including reliability, observability, testing, and maintainability.
• Strong grounding in securing AI and agentic systems, including least-privilege tool access, prompt-injection and misuse mitigation, secrets management, and safe handling of sensitive data.
• Ability to work across Software, Data, ML, Infrastructure, and Product teams.
• Strong debugging skills and comfort working with complex distributed systems.
• U.S. citizenship and the ability to obtain and maintain a security clearance.

Preferred Skills

• Experience with MCP, including MCP servers, clients, tools, resources, prompts, or connector patterns.
• Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, OpenAI APIs, Anthropic APIs, local/open-weight models, or similar AI tooling.
• Experience with vector databases, embeddings, retrieval systems, knowledge graphs, document processing, search, or RAG systems.
• Experience with LLM fine-tuning, supervised fine-tuning, preference tuning, synthetic data generation, evaluation datasets, or model benchmarking.
• Experience with Kubernetes, Docker, Ray, Airflow, Dagster, MLflow, Weights & Biases, Kafka, Postgres, S3-compatible storage, or similar infrastructure.
• Experience building internal platforms, developer tools, workflow automation systems, or data/ML infrastructure.
• Experience with human-in-the-loop workflows, approval systems, audit logs, policy enforcement, or safe tool invocation patterns.
• Experience integrating AI tools with engineering workflows such as GitHub, CI/CD, issue trackers, documentation systems, simulation platforms, or data lakes.
• Familiarity with autonomy, robotics, simulation, telemetry, perception, or defense technology workflows.
• Experience building secure AI systems for sensitive, regulated, government, defense, or enterprise environments.
• Active or prior security clearance.

Benefits:

• 100% Employer paid Health, Dental and Vision Insurance for you and your families
• Life Insurance (Employer Paid)
• Ability to participate in the companies 401k program (Matching)
• Unlimited PTO policy with an enforced 2 week minimum
• Equity Package
• Work / Home Office Stipend
• Global Entry
• 16 Week Paid Parental Leave
• Monthly Health and Wellness Stipend

Our Values: