Associate AI Engineer

IDT•Arlington, VA

4d•$74,000 - $150,000•Onsite

About The Position

Innovative Defense Technologies (IDT), provider of cutting-edge cloud-based integration, automated testing and data analysis for complex, mission-critical systems in the US Department of Defense (DOD), is seeking an Associate AI Engineer to be part of our Warfare Systems team and based out of our Arlington, VA or Mt. Laurel, NJ location. The Associate AI Engineer will design and deliver engineering-focused AI solutions that move beyond demos into reliable, mission-relevant systems. This role is ideal for an early-career engineer who is strong in Python, excited by LLMs and autonomous agents, and motivated to build end-to-end capabilities including MCP integrations, RAG pipelines, tool-using agents, and production-grade AI workflows. Clearance & Location Requirements: All applicants must be able to obtain/maintain an active Secret U.S. Security Clearance. This is an on-site position. Requiring at least 3 days in office, based out of our Arlington, VA or Mt. Laurel, NJ location.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Applied Mathematics, Artificial Intelligence, or a related technical field, or equivalent full-time professional experience
0–3 years of full-time professional experience in software engineering, machine learning engineering, AI engineering, or related technical roles
Ability to travel up to 10% of the time as needed
Proficiency in Python, including experience with core libraries such as NumPy and Pandas
Experience building software with one or more modern AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Semantic Kernel, or AutoGen
Familiarity with Large Language Models (LLMs), prompt engineering, agent orchestration, or conversational AI systems
Familiarity with retrieval systems, vector databases, embeddings, or RAG-based application design
Understanding of software engineering fundamentals, including version control, testing, debugging, and writing maintainable code
Ability to work independently on technical tasks while collaborating effectively in a team environment
Strong problem-solving skills, curiosity, and a builder mindset focused on turning ideas into working systems

Nice To Haves

Experience building agentic AI systems that use tools, memory, planning, or multi-step execution
Familiarity with Model Context Protocol (MCP) integrations or similar model-to-tool integration patterns
Experience deploying AI services, APIs, or workflows in cloud or production environments
Experience with vector databases, retrieval infrastructure, knowledge graph integration, or enterprise search
Exposure to evaluation methods for LLMs and agents, including reasoning quality, hallucination reduction, tool-use reliability, and retrieval performance
Experience with Docker, REST APIs, Git-based development workflows, or CI/CD pipelines
Experience working on defense, government, or other mission-oriented technical programs
Strong interest in building practical AI systems that must operate reliably in complex, real-world environments

Responsibilities

Design and Build AI Solutions: Design and implement end-to-end agentic AI systems that support planning, reasoning, tool use, and multi-step execution in real-world environments. Build modular, testable components that move from prototype to operational capability.
Integrate Models and Tools: Develop integrations across LLMs, APIs, data sources, and Model Context Protocol (MCP) interfaces to enable intelligent agents to interact with external systems, retrieve context, and take action safely and reliably.
Develop Retrieval Pipelines: Build and optimize Retrieval-Augmented Generation (RAG) pipelines that connect models to live knowledge sources, structured data, and enterprise content to improve factual grounding, contextual relevance, and response quality.
Engineer Conversational and Agentic Interfaces: Create conversational systems and intelligent agents with memory, contextual awareness, adaptive decision-making, and support for multi-turn user and system interactions.
Implement and Evaluate AI Workflows: Translate technical objectives into working pipelines, run experiments, evaluate agent behavior, and iterate on prompts, orchestration logic, retrieval quality, and system performance to improve reliability and usability.
Scope and Define Requirements: Gather, document, and validate technical and functional requirements from project artifacts, stakeholders, and mission needs to ensure feasibility, completeness, and alignment with operational goals.
Collaborate Across Teams: Work closely with engineers, technical leads, and mission stakeholders to integrate AI capabilities into broader software and system architectures. Participate in technical reviews, design discussions, and delivery planning.
Support Technical Quality: Contribute to testing, debugging, and performance optimization of AI-enabled applications, including edge cases involving context management, retrieval failures, tool execution, and orchestration logic.
Learn and Apply Emerging Practices: Stay current on advances in LLMs, agent frameworks, orchestration methods, and applied AI engineering practices, and bring that knowledge into practical system design and implementation.
Communicate Technical Work: Clearly document architectures, workflows, assumptions, and implementation decisions so that solutions are maintainable, explainable, and transferable across teams.