Interested in this AI/ML Engineer role at Innovative Defense Technologies?
Apply Now →Skills & Technologies
About This Role
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.
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.
Skills, Knowledge \& Expertise
Who You Are (Required):
- Bachelor’s Degree in Information Technology, Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Physics, Math, or equivalent full\-time professional experience; Master’s Degree in Engineering or other technical field highly desired
- 5\-10\+ years of professional experience in software engineering, machine learning engineering, AI engineering, or related technical roles
- Proficiency in Python, including experience with core libraries such as NumPy and Pandas
- Deep hands\-on experience with production local inference engines such as vLLM, SGLang, Triton Inference Server, or TensorRT\-LLM
- Experience building software with one or more modern AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Semantic Kernel, or AutoGen
- Experience with Linux and hardening (fapolicy/selinux/fips/etc)
- Experience with commercially available AI tooling/chat
- Experience with hosting LLM servers
- Experience with hosting different models (chat/embedding)
- Experience with distributed networking (reverse proxy/load balancing/firewalls/etc)
- Experience with containerization
- Ability to work independently on technical tasks while collaborating effectively in a team environment
- Ability to shift from one project to another in an agile work environment
- Strong leadership capabilities and skills
- Strong documentation skills
- Ability to travel up to 10% of the time, as needed
What Makes You Stand Out:
- Experience hosting LLM servers on local hardware
- Experience with various GPU architectures (NVIDIA preferred)
- Experience with AI Gateways
- Experience with LLM servers and model optimization (VRAM/layers/quantization/fasttensor/kv\-cache/parallel/etc)
- Experience with LLM toolcalling
- Experience with RAG
- Experience with vector stores
- Experience with ModelContextProtocol
- Experience with container orchestration
- Experience managing and directing personnel while maintaining cost and schedule targets
- Problem\-solving skills with the ability to navigate ambiguous situations
*If you see yourself reflected in this role and are excited about the impact you could make, we encourage you to apply! If you know someone who may be a great fit, please feel free to share this opportunity with your network.*
Pay Range\*: $114,000 \- $184,000 or $135,000 \- $231,000
This amount may not be reflective of actual compensation that may be earned as pay is dependent on a candidate’s experience, skills, and education. The posted range does not include bonuses, commissions, tips, or other benefits. IDT is often looking to place multiple candidates at various levels. Therefore, more than one pay range has been included, commensurate with experience.
Job Benefits
Why Work at Innovative Defense Technologies (IDT):
IDT is a growing company with a vibrant, entrepreneurial culture. We are headquartered in Arlington, VA with additional offices in Fall River, MA; Mount Laurel, NJ; and San Diego, CA. At each location, our employees work together in a modern, snack\-filled, and social office space, designing innovative solutions for our defense industry customers. We offer employees competitive pay and benefits including:* Generous benefits package
- Competitive PTO
- Paid holidays
- 401(k) with immediate vesting and matching
- 9/80 optional schedule (2nd and 4th Friday off every month)
- Tuition Assistance Reimbursement Program
- Professional Development Resources
- Pre\-Tax Commuter Benefits
- Organization\-Wide Monthly Tech Connect Events
- Annual Employee Recognition Awards
- Regular Social Events and Catered Lunches
EEO Statement:
IDT is an Equal Opportunity employer.
About Innovative Defense Technologies
Innovative Defense Technologies (IDT) is a leading defense technology company focused on rapidly delivering mission\-critical software and systems solutions to the U.S. government. The company specializes in automation, digital engineering, and enabling the rapid integration of advanced capabilities into complex weapon and combat systems. These essential solutions empower the warfighter to field, operate, and sustain decisive capability at speed and scale.
At IDT, our employees are developing advanced mission\-critical outcomes by consistently delivering high\-quality results, fostering innovation, applying rigorous problem\-solving, and effectively collaborating across multifaceted teams and stakeholders. Grounded in these competencies, IDT translates complex customer priorities into robust software solutions.
Salary Context
This $114K-$231K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Innovative Defense Technologies, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($172K) sits 21% below the category median. Disclosed range: $114K to $231K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Innovative Defense Technologies AI Hiring
Innovative Defense Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $231K - $231K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.