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Job Description
Our Information Technology organization is accelerating the company’s transformation toward intelligent, data‑driven operations. As artificial intelligence, advanced analytics, and automated decisioning increasingly define how we build, operate, and sustain enterprise systems, this role will serve as a senior leader shaping the direction of the company’s data and AI ecosystem.
The Director III, IT Service Owner for Artificial Intelligence \& Data Services, functions as the enterprise’s executive‑level authority on data governance, analytics, AI product development, and data platform strategy. Acting as the strategic owner and operational leader for all enterprise data and AI capabilities, this leader will define long‑range data strategy, ensure trust and stewardship of mission\-critical data assets, and drive the delivery of scalable, modern, secure AI‑enabled solutions.
This role partners across business areas, executive leadership, and technology domains to ensure data and AI services deliver measurable business value, operational efficiency, and competitive advantage. The director will also lead the organization in advancing data maturity, establishing strategic guardrails, developing technical talent, and ensuring that AI adoption adheres to security, governance, and ethical standards.
Key Responsibilities* Serve as the enterprise executive responsible for the strategy, performance, and outcomes of all AI and Data Services.
- Define and lead the enterprise data governance strategy, including policies, stewardship structures, quality frameworks, and adoption mechanisms that deliver meaningful incremental value.
- Own the roadmap, implementation, and operations of the enterprise data catalog, metadata management, and data lineage capabilities.
- Oversee security and access controls, ensuring data protection, compliant usage, and appropriate guardrails for AI models and analytics platforms.
- Develop and direct the execution of the enterprise AI product development pipeline, including use‑case selection, model lifecycle management, and value realization.
- Lead enterprise predictive analytics, HR analytics, and advanced reporting solutions that enable data‑driven business decisions.
- Establish strategic and operational governance for all enterprise data and AI services, ensuring alignment with corporate objectives, technology strategy, and risk posture.
- Define and maintain the technology roadmap for data platforms, AI infrastructure, reporting tools, and analytics ecosystems.
- Oversee support, sustainment, and continuous improvement of enterprise data systems and AI environments.
- Provide executive‑level communication and engagement to senior leaders, translating complex technical concepts into actionable recommendations.
- Influence, guide, and align leaders across the matrix—especially those outside direct reporting lines—to drive enterprise adoption of AI and data capabilities.
- Build and develop a high‑performing organization with deep data engineering, analytics, AI, and product delivery expertise.
- Establish and monitor service performance metrics, SLAs, risk indicators, and operational controls that ensure reliability and quality.
- Serve as an internal thought leader, advising executives on emerging AI practices, data opportunities, and enterprise\-wide modernization initiatives.
Required Education, Experience, \& Skills
- Bachelor’s degree, or equivalent combination of education and experience, in Computer Science, Engineering, Information Systems, Data Science, or related field; advanced degree strongly preferred.
- Minimum 12\+ years of progressive experience across data, analytics, AI, or enterprise technology leadership roles.
- Strong leadership and executive presence, capable of engaging and influencing senior stakeholders.
- Deep expertise in data analytics, predictive analytics, AI technologies, data governance, and modern data platforms.
- Proven ability to architect and lead data governance frameworks that deliver organizational value.
- Strong background in software development, technical program management, or enterprise technology delivery.
- Ability to balance long‑range strategic planning with short‑term operational execution.
- Exceptional communication skills, able to simplify complex technical concepts for non‑technical executives.
- Collaborative and innovative mindset, capable of driving transformation across a large, matrixed enterprise.
- Strong talent development and coaching capability to build future data and AI leaders.
- Ability to travel as needed, approximately 25%.
Preferred Education, Experience, \& Skills
- Demonstrated success implementing enterprise data governance frameworks and data cataloging solutions.
- Proven experience managing secure data environments, access controls, and compliance‑driven analytics ecosystems.
- Significant experience developing and delivering predictive analytics, reporting platforms, and AI products.
- Demonstrated ability to create and execute technology roadmaps supporting enterprise transformation.
- Proven capability in building and leading technical teams, fostering strong engineering and analytics talent.
- Strong background in software development, systems engineering, or technical program management.
Demonstrated ability to influence executives, lead cross‑functional initiatives, and drive alignment across a large matrix organization.
Pay Information
Full\-Time Salary Range: $200222 \- $340378
Please note: This range is based on our market pay structures. However, individual salaries are determined by a variety of factors including, but not limited to: business considerations, local market conditions, and internal equity, as well as candidate qualifications, such as skills, education, and experience.
Employee Benefits: At BAE Systems, we support our employees in all aspects of their life, including their health and financial well\-being. Regular employees scheduled to work 20\+ hours per week are offered: health, dental, and vision insurance; health savings accounts; a 401(k) savings plan; disability coverage; and life and accident insurance. We also have an employee assistance program, a legal plan, and other perks including discounts on things like home, auto, and pet insurance. Our leave programs include paid time off, paid holidays, as well as other types of leave, including paid parental, military, bereavement, and any applicable federal and state sick leave. Employees may participate in the company recognition program to receive monetary or non\-monetary recognition awards. Other incentives may be available based on position level and/or job specifics.
About BAE Systems, Inc.
BAE Systems, Inc. is the U.S. subsidiary of BAE Systems plc, an international defense, aerospace and security company which delivers a full range of products and services for air, land and naval forces, as well as advanced electronics, security, information technology solutions and customer support services. Improving the future and protecting lives is an ambitious mission, but it’s what we do at BAE Systems. Working here means using your passion and ingenuity where it counts – defending national security with breakthrough technology, superior products, and intelligence solutions. As you develop the latest technology and defend national security, you will continually hone your skills on a team—making a big impact on a global scale. At BAE Systems, you’ll find a rewarding career that truly makes a difference.
This position will be posted for at least 5 calendar days. The posting will remain active until the position is filled, or a qualified pool of candidates is identified.
Salary Context
This $200K-$340K range is above the 75th percentile 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 BAE Systems USA, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($270K) sits 24% above the category median. Disclosed range: $200K to $340K.
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.
BAE Systems USA AI Hiring
BAE Systems USA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Falls Church, VA, US, Boulder, CO, US. Compensation range: $226K - $340K.
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
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