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About This Role
Job Description
BAE Systems is seeking an IT Service Owner – Data \& AI Platforms responsible for the strategy, architecture, reliability, security, and full lifecycle management of the enterprise data and AI platform ecosystem. This role serves as the authoritative owner of the technology stack enabling ingestion, transformation, storage, governance, access, and AI/ML platform capabilities. The leader in this role will define the technology roadmap for the enterprise data and AI platforms \- powering the company’s core data operations through technologies such as Snowflake, Dataiku, Power BI, Semarchy, and Collibra. The Service Owner ensures the platform is modern, scalable, integrated, cost\-efficient, and delivers the availability and security required for a regulated defense environment.
Responsibilities* Own the end\-to\-end performance, scalability, and security of the enterprise data and AI platform stack, including Snowflake, Dataiku, Power BI, Semarchy, and Collibra.
- Establish engineering standards, integration patterns, and architectural guardrails for all platform components.
- Define and govern the multi\-year roadmap for services such as ingestion, ETL/ELT frameworks, orchestration, compute engines, storage layers, cataloging, access control, and observability.
- Drive modernization initiatives including lakehouse architectures, cloud\-native data engineering, containerization, automation, and reliability engineering.
- Direct platform architecture across batch, micro\-batch, and streaming data flows.
- Ensure robust integration between Snowflake, Dataiku, Power BI, Semarchy, Collibra, and adjacent enterprise systems.
- Oversee reliability engineering, monitoring, observability, incident response, and change management for all platform services.
- Maintain strong disaster recovery, platform resiliency, and high\-availability designs.
- Implement zero\-trust aligned data access, encryption, masking, and audit logging across all platform technologies.
- Govern enterprise metadata, lineage, quality, and stewardship through Collibra and Semarchy.
- Provide clear, executive\-level communication of platform investment plans, risks, and architectural decisions.
- Influence leaders across cybersecurity, infrastructure, engineering, and business teams, including those outside the direct reporting line.
- Lead, mentor, and develop high\-performing engineering, SRE, and platform operations teams.
- Oversee vendor management and financial stewardship for Snowflake, Dataiku, Power BI, Semarchy, Collibra, and related partners
Competencies* Executive presence with the ability to influence senior stakeholders on complex platform decisions.
- Deep expertise in enterprise data platforms including Snowflake, Dataiku, Power BI, Semarchy, and Collibra.
- Communication excellence, capable of translating technical platform decisions for non\-technical executives.
- Strong talent\-development capability and ability to build engineering organizations.
- Ability to lead engineering and platform operations teams in large, complex, matrixed enterprises.
Required Education, Experience, \& Skills
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (advanced degree preferred).
- 12\+ years of progressive experience in enterprise data platforms, cloud platforms, MDM, governance, or systems engineering roles.
- Proven experience leading platform engineering and operations for Snowflake, Dataiku, Power BI, Semarchy, and Collibra.
- Familiarity with responsible AI, governance frameworks, and MLOps practices.
- Deep expertise in secure data architectures, identity and access controls, and compliance\-aligned platform management.
- Strong background in software development, systems engineering, or technical program management.
- Demonstrated capability to develop and execute enterprise\-scale platform roadmaps.
- Proven ability to influence senior leaders and shape decisions across a matrixed environment.
Preferred Education, Experience, \& Skills
- Experience in highly regulated industries such as defense, aerospace, or government.
- Experience with large\-scale cloud data ecosystems, container orchestration, and observability platforms.
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Pay Information
Full\-Time Salary Range: $153377 \- $260743
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 $153K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $153K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BAE Systems USA AI Hiring
BAE Systems USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Falls Church, VA, US. Compensation range: $260K - $260K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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