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About This Role
At PMAT, we work on mission critical systems that directly support the warfighter, designing, building, and securing modern digital capabilities across cloud, data, and software environments. Our teams tackle complex, real-world challenges where delivery matters more than theory, and innovation is driven by curiosity, collaboration, and purpose.
In this role, you’ll contribute to enterprise data, artificial intelligence (AI), machine learning (ML), and advanced technology capabilities, working alongside engineers and operators to deliver resilient, forward-leaning solutions in support of national defense.
About the role: PMAT is seeking a technically accomplished and commercially oriented Vice President of Technology AI and ML to lead the company’s enterprise-wide data, AI, and ML strategy. Reporting directly to the Chief Operations Officer (COO), the VP of Technology will serve as PMAT’s senior executive responsible for transforming data and AI capabilities into strategic business assets. You’ll establish scalable data and AI architectures, integrate AI/ML into PMAT’s products, platforms, and operations, and help translate emerging technologies into practical, mission-focused capabilities for Department of Defense (DoD), Intelligence Community (IC), and other federal customers. Working across executive leadership, Growth, Engineering, Program Management, SBIR, and Advanced Technology, you’ll help identify new market opportunities, develop differentiated technical capabilities, transition innovations from research and prototyping into operational deployments, and connect technology investments to measurable business and mission outcomes.
Responsibilities:
- Develop and execute PMAT’s enterprise-wide data, AI, and ML strategy, including a long-term technology roadmap encompassing generative and agentic AI, predictive analytics, computer vision, natural language processing, intelligent automation, and emerging technologies. Advise the COO and executive leadership on technology trends, market opportunities, investment priorities, and the strategic implications of AI and data-driven capabilities.
- Lead the design and implementation of scalable, secure, and modular AI/ML architectures and establish technical standards for data ingestion and integration, model development, deployment, orchestration, monitoring, and lifecycle management. Direct solutions involving large language models (LLMs), retrieval-augmented generation (RAG), autonomous and agentic systems, predictive analytics, advanced decision support, MLOps, LLMOps, and DataOps.
- Guide development of cloud-native, hybrid-cloud, edge, disconnected, classified, and tactical AI architectures; evaluate and integrate commercial, open-source, and government-developed technologies; oversee prototyping and proof-of-concept development; and ensure architectural alignment across PMAT’s technology portfolio, including Overmatch and Tactical Edge Node capabilities.
- Establish PMAT’s enterprise data strategy, including data architecture, governance, quality, integration, security, interoperability, and lifecycle management. Lead development of scalable data infrastructure, pipelines, data lakes/lakehouses, streaming architectures, and enterprise data platforms while establishing governance standards for ownership, classification, access, lineage, quality, retention, and interoperability.
- Partner with the COO, Chief Growth Officer, and executive leadership to develop growth strategies leveraging PMAT’s AI/ML, data, and advanced analytics capabilities. Identify and support federal business opportunities, develop differentiated technical solutions and value propositions, participate in customer engagements and strategic partnerships, and support captures and proposals through technical strategy, solution architecture, innovation roadmaps, and differentiated AI/ML approaches.
- Partner with PMAT’s SBIR and Advanced Technology teams to translate customer mission requirements into technical architectures, development roadmaps, prototypes, demonstrations, and operational solutions. Leverage SBIR/STTR and other technology-development programs to mature innovative capabilities, develop reusable AI/ML components, frameworks, models, platforms, and intellectual property, and transition successful innovations into operational capabilities, Phase III opportunities, and production programs.
- Establish and oversee PMAT’s enterprise AI governance framework, ensuring AI/ML solutions align with applicable federal requirements, relevant NIST AI Risk Management Framework principles, cybersecurity standards, and agency-specific requirements. Integrate security, privacy, explainability, model validation, human oversight, and risk management into AI/ML development and address model vulnerabilities, data poisoning, adversarial threats, model drift, and third-party technology risk.
- Build and lead high-performing teams across data science, AI/ML engineering, data engineering, AI architecture, and advanced analytics. Establish organizational structure, talent strategy, development standards, operating budgets, investment priorities, and performance measures while driving technical excellence, innovation, accountability, knowledge sharing, operational efficiency, and scalable delivery across programs and customers. Establish and achieve measurable outcomes across AI/ML strategy execution; revenue growth and qualified pipeline; capture participation and contract awards; product and technology development; enterprise AI/ML adoption; operational efficiency; technical performance and scalability; talent acquisition, retention, and development; financial performance and return on technology investments; AI governance and security; strategic customer engagement; SBIR/STTR support; Advanced Technology collaboration; and the transition of AI/ML innovations into funded initiatives, Phase III awards, operational deployments, and production programs.
About Us: PMAT is an innovative small business founded with a passion for developing forward-leaning solutions from exceptional people that increase the mission's capability. We focus on designing and building impactful digital solutions that utilize modern cloud, data, and software concepts. Our passion is working on complex and progressive challenges such as edge platform computing, containerizing legacy platforms, distributed data platforms, or heterogeneous data analysis.
We recruit, retain, and foster a team motivated to pursue passions, investigate new ways of doing things, and embody an innovative and entrepreneurial spirit. We believe in being curious about every element of a problem and experiment relentlessly. We foster continuous learning in an environment that encourages positive collaboration and expands our capabilities. We tap into collective intelligence, acknowledging that the most brilliant people may not be in the room. Above all else, we believe that delivering and demonstrating is more potent than a sheet of paper. We are passionate about mission-centric design and delivering effective capabilities to and for the warfighter.
Whether you’re an experienced engineer or just beginning your career, you’ll work alongside experts who are committed to solving mission-critical problems. If you’re passionate about using your skills to make a real difference, apply today and become part of a team that’s shaping the future of defense technology!
Required Skills and Experience:
- 12+ years of progressive experience in data engineering, AI/ML, software engineering, advanced analytics, or related technology disciplines, including significant leadership experience.
- A Senior Director at a large organization.
- Demonstrated experience developing and executing enterprise data, AI, or ML strategies and translating them into operational capabilities.
- Deep understanding of modern AI/ML architectures, including generative AI, large language models, predictive analytics, machine learning, and intelligent automation.
- Experience designing and implementing enterprise data platforms, scalable AI architectures, and production-grade AI/ML solutions.
- Experience leading multidisciplinary technical teams and complex technology-development initiatives.
- Demonstrated ability to translate complex technologies into business strategies, customer solutions, and measurable financial or mission outcomes.
- Experience supporting federal customers, preferably within the Department of Defense, Intelligence Community, or national security sectors.
- Experience contributing to business development, capture, technical proposals, or commercialization of technology capabilities.
- Strong executive communication, strategic planning, organizational leadership, and stakeholder-management skills.
- Demonstrated ability to establish and measure organizational and technology performance across strategy execution, business growth, technology development and transition, technology adoption, operational efficiency, technical excellence, organizational development, governance, security, and customer impact.
- Ability to operate as a member of executive leadership and connect technology strategy to business growth, operational performance, customer mission outcomes, and long-term enterprise value.
Preferred Skills and Experience:
- Previous experience as a VP of Technology, CTO, VP of Engineering, or comparable senior executive responsible for enterprise AI/ML or data strategy.
- Experience implementing AI/ML solutions in classified, air-gapped, edge, tactical, or other highly constrained environments.
- Experience with DoD and IC technology environments, including cybersecurity, accreditation, deployment, rapid capability development, and technology transition requirements.
- Familiarity with SBIR/STTR programs and DoD technology transition pathways.
- Experience with major cloud and AI ecosystems such as AWS, Microsoft Azure, Google Cloud, Databricks, and NVIDIA.
- Experience with modern AI development frameworks and technologies such as PyTorch, TensorFlow, Hugging Face, LangChain, vector databases, and model orchestration platforms.
- Experience developing reusable AI-enabled products, software platforms, or intellectual property for application across multiple federal customers.
- Demonstrated success building strategic technology partnerships and differentiated solutions for competitive federal procurements.
Education and Certification Requirements:
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Information Systems, or a related technical discipline.
- Master’s degree or Ph.D. in Artificial Intelligence, Computer Science, Data Science, Machine Learning, or a related discipline preferred.
Citizenship and Clearance requirements:
- U.S. Citizenship required
- No dual citizenship
- Ability to obtain and maintain an active DoD TS/SCI security clearance
Location/Address:
- Remote - Washington, D.C.
Travel & Passport:
- Under 35% travel
- CONUS
Work Environment: PMAT offices as needed. In some cases, work in a government facility may be required. Travel may be required for customer engagement, team coordination, and potentially for business development.
PMAT is an equal-opportunity employer. We believe in hiring a diverse workforce and sustaining an inclusive, people-first culture. We are committed to non-discrimination on any protected basis, such as disability and veteran status, or any other basis covered under applicable law.
Salary Context
This $150K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At PMAT Inc., 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 $212,000 based on 11,866 positions with disclosed compensation. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $150K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
PMAT Inc. AI Hiring
PMAT Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
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