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Entry Level Innovative Defense Technologies Jobs

Idtus

Field Test Engineer - JobID-0029

San Diego, CA · On-site

$74 - $150/hr

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 ...

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Entry Level Innovative Defense Technologies information

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$29.5K

$49.6K

$60K

How much do entry level innovative defense technologies jobs pay per year?

As of Sep 5, 2026, the average yearly pay for entry level innovative defense technologies in the United States is $49,574.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,000.00 and $54,500.00 per year, depending on experience, location, and employer.

What is the difference between Entry Level Innovative Defense Technologies vs Entry Level Systems Analyst?

AspectEntry Level Innovative Defense TechnologiesEntry Level Systems Analyst
Required CredentialsBachelor's in Engineering, Computer Science, or related field; security clearance may be preferredBachelor's in Information Systems, Computer Science, or related field; certifications like CompTIA or Cisco beneficial
Work EnvironmentDefense contractors, government agencies, R&D labsCorporate IT departments, consulting firms, government agencies
Employer & Industry UsagePrimarily in defense, aerospace, and security sectorsBroadly in technology, finance, healthcare, and government sectors

Entry Level Innovative Defense Technologies focuses on developing cutting-edge defense solutions within the defense industry, often requiring security clearances. In contrast, Entry Level Systems Analysts work across various industries analyzing and improving IT systems. Both roles require technical skills but differ in industry focus and specific credentials.

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Entry Level Innovative Defense Technologies jobs near you
Infographic showing various Entry Level Innovative Defense Technologies job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $49,574 per year, or $23.8 per hour.

AI Implementation Engineer - JobID-0245

Innovative Defense Technologies (IDT)

Arlington, VA • On-site

$114K - $231K/yr

Other

Re-posted 27 days ago

Job description

About The Role:
Innovative Defense Technologies (IDT), a leading defense technology company, is seeking an AI Implementation Engineer to be part of our Warfare Systems team and based out of our Arlington, VA or Mount Laurel, NJ location.
The AI Implementation Engineer will design and deliver engineering-focused AI solutions that move beyond demos into reliable, mission-relevant systems. This role is ideal for an engineer who has extensive experience with commercially available AI tooling/chat, hosting LLM servers and has demonstrated ability 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 location or Mt. Laurel, NJ location.
Department Engineering Employment Type Full Time Location Arlington, VA Address 4401 Wilson Boulevard Suite 810, Arlington, Virginia, 22203 Open in Google Maps Workplace type Hybrid Compensation $114,000 - $231,000 / year Key Responsibilities
What You Will Do:
  • Design and Build AI Solutions for On-Prem systems in Air-Gapped environment: 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 for On-Prem systems in Air-Gapped environment: 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 for On-Prem systems in Air-Gapped environment: 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 for On-Prem systems in Air-Gapped environment: 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 for On-Prem systems in Air-Gapped environment: 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.
  • Architect local infrastructure to size, config, and optimize local CPU/GPU workloads, utilizing quantization techniques to maximize throughput, etc.
  • Orchestrate disconnected environments, design and maintain offline model update pipelines, local package mirrors, etc.
  • 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.

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