Agentic AI Engineer

Noda

full-time

Posted on: 9/3/2025

Location: 🇺🇸 United States

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Mid-LevelSenior

Distributed SystemsPythonReact

About the role

NODA is a veteran-owned, venture-backed company building distributed orchestration for unmanned systems across air, sea, land, and space.
Agentic AI Engineer to design and implement intelligent agents for adaptive mission planning and multi-domain orchestration.
Translate mission intent into actionable tasks with dynamic replanning and transparent reasoning for operators.
Integrate LLM orchestration frameworks (e.g., LangChain, ReAct-style planners) into NODA’s orchestration stack.
Develop reasoning frameworks for task decomposition, scheduling, and allocation in collaboration with autonomy teams.
Implement human-in-the-loop workflows for operator trust and explainability.
Manage full lifecycle of AI agents (models, prompts, tools, memory) with safe deployment and rollback.
Pipeline sensor and mission data into structured AI-ingestible formats.
Validate agent behaviors through simulation-in-loop and hardware-in-loop testing prior to live deployment.
Build and maintain evaluation, monitoring, and logging systems to track performance, cost, and reliability.
Integrate external tools/APIs with version control and dependency management.
Design CI/CD, canary/blue-green deploys, and feature-flag release pipelines for safe production iteration.
Contribute to secure and resilient agent execution in denied, degraded, and contested communications environments.

3+ years of experience in AI/ML applications (LLM orchestration, autonomous decision-making, or planning systems)
Proficiency in Python and frameworks such as LangChain or equivalent
Familiarity with constraint solving, planning algorithms, or symbolic reasoning
Familiarity with distributed systems and real-time orchestration
Excellent problem-solving skills and ability to collaborate across disciplines
U.S. Citizenship with ability to obtain a clearance
(Preferred) Experience with multi-agent coordination frameworks or reinforcement learning for planning
(Preferred) Understanding of secure coding and adversarial robustness in AI-driven systems
(Preferred) Exposure to autonomous vehicles (UAVs, USVs, UUVs)
(Preferred) Familiarity with simulation-in-loop or hardware-in-loop testing
(Preferred) Hands-on experience with robotics/autonomy frameworks (ROS2, Gazebo, navigation stacks)
(Preferred) Experience deploying AI/ML logic to edge devices (Jetson, Raspberry Pi, or similar)
(Preferred) Background in data engineering or structured data prep for AI ingestion
(Preferred) Contributions to open-source AI or robotics projects