Artificial Intelligence Engineer
About The Position
The Artificial Intelligence Engineer brings advanced AI capabilities into production systems by integrating models, inference endpoints, agent-orchestration frameworks, and intelligent workflows into complex, distributed software environments. The role applies software engineering principles to ensure AI features operate reliably, securely, and efficiently at scale. The engineer helps evolve LLM-driven solutions into goal-oriented, multi-agent architectures using agent-orchestration frameworks, Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and fine-tuned Small Language Models (SLMs). The role also contributes to evaluation and monitoring strategies, refines integration workflows, collaborates with cross-functional engineering teams, and develops scalable AI capabilities for operational, mission-ready solutions.
Requirements
- At least 3 years of experience developing and maintaining production-grade software systems in Python.
- Experience designing and implementing AI agent architectures using LangChain, LangGraph, AutoGen, MCP, or A2A.
- Experience building RAG solutions with Knowledge Graphs and fine-tuning SLMs using ONNX, GGML, or Ollama.
- Experience integrating LLMs, external APIs, and distributed services into production with optimized inference workflows.
- Experience developing AI evaluation frameworks, integration tests, and production validation using ReAct patterns and human-in-the-loop methodologies.
- Knowledge of secure HTTP APIs, OAuth2, JWT, API Keys, containerization, and CI/CD pipelines.
- Ability to travel up to 25% of the time.
- TS/SCI clearance.
- Bachelor’s degree in Computer Science, Engineering, or Data Science.
- DoD 8140 Level II certification, such as Security+, CySA+, or CISSP.
Nice To Haves
- Experience supporting DoD clients or other federal mission environments.
- Experience with advanced agentic patterns, including prompt caching, guardrails, agent memory, Model Context Protocol (MCP), and agent deployment strategies.
- Experience developing end-to-end chatbot or agent-to-agent (A2A) systems.
- Experience with enterprise data and machine learning platforms such as Databricks or Snowflake.
- Experience building or managing large-scale data processing pipelines.
- Master’s degree in a related field.
Responsibilities
- Integrate models, inference endpoints, agent-orchestration frameworks, and intelligent workflows into complex, distributed software environments.
- Apply software engineering principles to ensure AI features operate reliably, securely, and efficiently at scale.
- Help evolve LLM-driven solutions into goal-oriented, multi-agent architectures using orchestration frameworks, RAG, Knowledge Graphs, and fine-tuned SLMs.
- Contribute to evaluation and monitoring strategies and refine AI integration workflows.
- Collaborate with cross-functional engineering teams to develop scalable, mission-ready AI capabilities.
Benefits
- Health, life, and disability benefits.
- Financial and retirement benefits.
- Paid leave.
- Professional development.
- Tuition assistance.
- Work-life programs.
- Dependent care.
- Recognition awards for exceptional performance and demonstration of company values.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
