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About This Role
Chandler, AZ, United States
Leader of AI and Data Leader
Function/Department: Information Technology / Data & AI
Reports to: Chief Information Officer (CIO) & Vice President, IT
Location: Chandler, AZ or Hollister, CA — fully on-site
FLSA: Exempt
Job Summary
PacSci EMC is seeking a hands-on AI engineering leader to accelerate enterprise AI adoption and build the governed data foundations that enable practical, secure business solutions. This leader personally develops, deploys, and sustains production AI and data capabilities while leading a small team across data engineering, architecture, and application and analytics development.
The AI and Data Leader leads technical architecture and data readiness for the Palantir Foundry initiative in partnership with the business SME leader and PMO, while advancing enterprise data stewardship, lifecycle management, and self-service tools. The role combines technical execution, team development, and executive influence in an evolving environment. It also participates in the Ralliant Technology & Growth Council, with up to 20% allocation, contributing to enterprise architecture reviews, innovation, standards, and cross-operating-company technology direction.
Responsibilities
- Evaluate and recommend secure, compliant AI tools; personally implement and operationalize priority solutions that improve productivity and enable business teams to use AI effectively.
- Identify and prioritize AI opportunities across Planning, Buying, Manufacturing Operations, Factory Management, Engineering, PMO, and other business functions; translate operating challenges into practical solutions and measurable outcomes.
- Lead and develop a small team across data engineering, architecture, and Power BI/Power Platform development; provide technical coaching, establish delivery priorities, and build team capability as business needs evolve.
- Lead technical architecture, data readiness, and implementation support for Palantir Foundry in partnership with the business SME leader, who retains overall program delivery accountability, and PMO; coordinate appropriate technology resources and architectural decisions.
- Establish enterprise data warehousing, stewardship, and lifecycle-management practices, including labeling, lineage, retention, role-based access, auditability, and clearly defined data ownership.
- Use Microsoft Purview to support data governance and lifecycle-management practices across priority data domains.
- Build and maintain production-grade pipelines, transformations, data models, and reusable integration patterns; develop governed ontologies, lightweight applications, and self-service data products as business priorities require.
- Partner with manufacturing engineering and other technical specialists on SCADA/OT data integration, reference data products, and anomaly-detection use cases; establish data architecture standards and document interfaces and operational runbooks.
- Enable business users through training, practical playbooks, quick-start templates, and hands-on collaboration; support citizen development within appropriate access and compliance guardrails.
- Partner with analytics developers to deliver dashboards, signals, and triggers that improve operational visibility and actionability, including order execution and cost tracking.
- Own reliability of AI and data assets through monitoring, observability, performance tuning, troubleshooting, and ongoing production support; implement reusable quality checks and alerts.
- Present technology options and recommendations to leaders with clear cost, impact, risk, and implementation tradeoffs; collaborate on decisions and balance immediate delivery needs with enterprise standards.
- Define baselines and track solution adoption and business impact, including hours saved, manual effort reduced, automated transactions, efficiency, revenue enablement, and supply-chain execution performance.
- Maintain an actionable opportunity backlog, data product catalog, access reviews, and governance routines aligned with PMO practices; contribute to Ralliant technology and architecture forums.
First 6 to 12 Months Focus and Deliverables
- Build relationships with manufacturing, quality, program management, supply chain, and other business partners to understand pain points and establish a prioritized AI opportunity pipeline.
- Support the Palantir initiative through sound technical architecture, data readiness, and delivery of initial governed data products and Foundry assets.
- Implement an enterprise data layer strategy with clear ownership, warehousing standards, lifecycle practices, and appropriate governance and access safeguards.
- Expand secure self-service AI tools and adoption through training, practical use cases, and reusable development standards.
- Demonstrate sustained production delivery and measurable business improvements in productivity, manual effort, efficiency, revenue enablement, or supply-chain execution.
Qualifications
- 5+ years in analytics, data platforms, AI enablement, or data governance roles, with proven hands-on delivery.
- Demonstrated personal experience building, deploying, and sustaining production AI and data solutions, including production-grade pipelines and appropriate operational safeguards.
- Strong hands-on proficiency with SQL and Python/PySpark; experience with data modeling, orchestration, and observability.
- Experience with Microsoft Purview and enterprise data governance, stewardship, or lifecycle management.
- Demonstrated ability to learn enterprise data platforms, guide architectural decisions, and coordinate technical delivery across business and technology teams.
- Working knowledge of Power BI or similar analytics tools and the ability to partner with developers on governed self-service analytics.
- Strong cross-functional influence and executive communication; ability to explain technical concepts clearly to non-technical audiences and support recommendations with evidence and business impact.
- Ability to lead technical work, coach colleagues, and collaborate effectively while remaining personally involved in engineering and delivery.
- Comfort operating in an evolving environment, prioritizing amid ambiguity, developing practical prototypes, and carrying solutions through implementation and ongoing support.
Preferred Qualifications
- Experience with Palantir Foundry, including pipelines, ontology, and application development; strongly preferred.
- Background in aerospace and defense or other highly regulated industries with strict data and compliance requirements, such as ITAR, FAR/DFARS, or DCAA.
- Familiarity with controlled unclassified information (CUI), FedRAMP requirements, and secure cloud deployment practices.
- Working knowledge of SCADA/OT data environments, ERP/MES/PLM data structures, and manufacturing execution workflows.
- Experience building AI or data capabilities in a startup, technology disruptor, or similarly resource-constrained environment, with demonstrated implementation results.
- Familiarity with Lean principles and structured problem solving; ability to balance rapid delivery with compliance and sustainable operations.
Employment Eligibility
Due to U.S. Government contract requirements applicable to this position, only U.S. citizens are eligible for employment in this role.
#LI-FP1 Ralliant Corporation Overview
Ralliant, originally part of Fortive, now stands as a bold, independent public company driving innovation at the forefront of precision technology. With a global footprint and a legacy of excellence, we empower engineers to bring next-generation breakthroughs to life — faster, smarter, and more reliably. Our high-performance instruments, sensors, and subsystems fuel mission-critical advancements across industries, enabling real-world impact where it matters most. At Ralliant we're building the future, together with those driven to push boundaries, solve complex problems, and leave a lasting mark on the world.
About PacSci EMC
Pacific Scientific Energetic Materials Company (PacSci EMC), a Ralliant Corporation (NYSE: RAL) operating company, makes critical missions possible. From concept to demonstration to delivery, we provide leading-edge energetic materials, devices and subsystems for use in military and commercial applications. With engineering and manufacturing capability in Arizona and California, our associates work together to solve the toughest technical challenges with a dedication to Safety First, Quality Every TimeTM. We enable success for innumerable missions on a daily basis, impacting the lives of military personnel, law enforcement officers, commercial airline pilots, astronauts, and oil field operators. For nearly a century, PacSci EMC’s experience and expertise has been relied upon in the times When Milliseconds MatterTM. For more information, go to www.psemc.com. PacSci EMC is an equal opportunity employer. We do not discriminate against any applicant or employee on the basis of race, sex, religion, age, national origin, color, disability, or veteran status. Nonetheless, US citizenship is a requirement for this position due to U.S. Government contractual requirements. We are committed to providing a workplace free of any discrimination or harassment. We all have a role in building a community where everyone belongs. Where you can speak your mind, be yourself, and feel supported. It’s how we achieve more together — for each other, for our customers, and for the world. https://www.Ralliant.com/inclusion-diversity.
We Are an Equal Opportunity Employer. Ralliant Corporation and all Ralliant Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Ralliant and all Ralliant Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at [email protected].
Pay Range
The salary range for this position (in local currency) is 130200.00-241800.00
Is this role subject to ITAR?
The essential duties of this position require adherence to U.S. Government export control regulations. Accordingly, candidates must either be U.S. Persons (i.e., U.S. citizens, U.S. lawful permanent residents, or protected individuals as defined by 8 U.S.C. 1324b(a)(3)) or be prepared to collaborate with the company in securing the necessary U.S. government export authorizations. While the company encourages all interested applicants to apply, please be aware that ongoing employment is dependent upon obtaining the appropriate government export authorizations.
Salary Context
This $130K-$241K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pacific Scientific Energetic Materials Company, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $130K to $241K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Pacific Scientific Energetic Materials Company AI Hiring
Pacific Scientific Energetic Materials Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chandler, AZ, US. Compensation range: $241K - $241K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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