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About This Role
Position Summary
The Head of AI for the Business Unit is the senior leader responsible for the BU's AI strategy and execution, with a mandate to convert AI capability into measurable business performance. Reporting to the BU President, this role owns the BU's AI roadmap end\-to\-end: from mapping the BU's processes, to scoring automation opportunities by ROI and feasibility, to building and deploying AI\-powered solutions, to driving adoption and measuring the resulting business impact across productivity, quality, speed, margin, and decision\-making.
This is a hands\-on leadership role. The Head of AI manages a small team of Forward Deployed Engineers (FDEs) embedded in the BU's departments, partners with the central AI Enablement function at corporate for platform support, and engages with the company\-wide AI Steering Committee on cross\-BU patterns. We are looking for a leader senior enough to be credible with the BU President and to operate a function independently — but hands\-on enough to spend meaningful time on the shop floor, in the data, and in front of users.
Key Responsibilities \& Essential Functions
- Own the BU's AI roadmap, accountable to the BU President for measurable business impact across growth, margin, productivity, quality, speed, and decision\-making
- Lead the BU's end\-to\-end process mapping effort — building the complete inventory of departmental processes that serves as the funnel for AI applications
- Score and prioritize automation candidates by ROI, feasibility, adoption readiness, data availability, and business criticality; defend prioritization decisions to BU leadership
- Recruit, manage, and develop a team of department\-level Forward Deployed Engineers — initially 1–3 per BU onshore/offshore, scaling with demonstrated readiness
- Build and deploy AI applications using Claude and other approved tools; design measurement frameworks to track impact per application, including baseline, expected benefit, user adoption, and realized business impact
- Drive enterprise AI tool adoption across the BU; serve as the local champion and trainer
- Partner with the central AI Enablement function (corporate IT) for tools, data access, vendor agreements, governance, cybersecurity, and responsible AI standards — escalating blockers through the company's Unblock queue
- Represent the BU in the company\-wide AI Steering Committee on cross\-BU patterns, shared learnings, and structural improvements
- Track and report against KPIs: percent of BU processes mapped, percent automated by milestone, business impact per deployed application, adoption by target users, Finance\-validated business impact, and scalability of deployed application
Requirements:
Required Qualifications
- 7–12 years of progressive experience in technology, operations, or strategy roles
- Bachelor's degree in engineering, computer science, operations research, industrial engineering, or a related technical field
- Demonstrated success in operational improvement or transformation programs — Lean, Six Sigma, BPM, or equivalent with experience building business cases, measuring ROI, and sustaining adoption after implementation
- Hands\-on familiarity with modern AI/ML technologies, especially large language models (Claude, ChatGPT, Gemini) and their practical applications to business processes, including workflow automation, knowledge retrieval, decision support, and productivity improvement
- Track record of leading teams of 2–5 people in cross\-functional environments
- Comfort and credibility across all levels of an industrial organization — from shop floor operators and department managers to BU executives
Preferred Qualifications
- Prior experience as a or with Forward Deployed Engineers, applied AI engineers, or solutions architects at a leading AI or analytics company.
- Manufacturing or industrial operations background — ideally aerospace, automotive, electronics, or fasteners \- with exposure to commercial, engineering, quality, supply chain, or plant\-floor workflows
- Familiarity with ERP environments (SAP, Oracle, Epicor) sufficient to know where data lives and how to extract it
- MBA, MS, or other advanced technical or business degree
- Prior P\&L responsibility, product ownership, or general management experience
Skills \& Competencies
- High agency. Comfortable taking ambiguous problems, defining solutions, and shipping with limited supervision.
- Bias to action. Prefers rough solutions in production over polished ones in drafts, and iterates with real users.
- Credibility across the stack. Can present to a board and debug a workflow on the shop floor the same day.
- AI realist, not AI evangelist. Focused on practical applications with measurable ROI; skeptical of hype.
- Change leader. Able to create trust, drive adoption, and help teams change how work gets done without making AI feel threatening or imposed
- Operating instinct. Thinks in processes, metrics, and unit economics — not in slideware.
Physical \& Environmental Requirements
- Able to sit or stand for 8 hours a day.
- Unairconditioned manufacturing facility and air\-conditioned office
- On\-site 5 days per week
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PennEngineering, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PennEngineering AI Hiring
PennEngineering has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waterford, MI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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