Interested in this AI/ML Engineer role at DuPont?
Apply Now →Skills & Technologies
About This Role
Opened Recently
Job Type
Experienced
Postal Code
19805
Wilmington, Delaware
Job Id
249685W
Category
EngineeringPosted On \- 07/13/2026
Job available in 4 locations
At DuPont, our purpose is to empower the world with essential innovations to thrive. We work on things that matter. Whether it’s providing clean water to more than a billion people on the planet, producing materials that are essential in everyday technology devices from smartphones to electric vehicles, or protecting workers around the world. Discover the many reasons the world’s most talented people are choosing to work at DuPont. Why Join Us \| DuPont Careers
Role Purpose
The AI Leader for Operations will define, operationalize, and scale the use of artificial intelligence and advanced analytics across global operations to drive measurable improvements in safety, quality, delivery, inventory, asset reliability, and cost.
This role serves as the primary product owner for AI in Operations, bridging operations leadership, Digital/IT, data science, and product teams to translate concrete operational problems into AI‑enabled solutions that are embedded into ways of working, adopted at scale, and sustained over time.
The role is accountable for the Operations AI roadmap, product lifecycle management from intake through scaled deployment, and value realization—working within DuPont’s enterprise AI strategy, architecture, and Responsible AI guardrails.
Key Responsibilities – Product Ownership, Roadmap \& Value Delivery
- Develop and execute the Operations AI roadmap aligned to business and OpEx priorities, including multi‑year sequencing across sites and regions.
- Translate operational objectives into a balanced AI product backlog/portfolio with clear ROI, scalability, risk, and change‑impact criteria; identify high‑value use cases to prevent fragmentation and stop low‑value efforts early.
- Own value realization and sustained business impact with measurable KPIs
- Ensure successful use cases progress from pilot to productized capability to scaled, enterprise deployment with reuse across the footprint.
Operational Integration \& Execution
- Lead end‑to‑end deployment of AI solutions from design through adoption, including process redesign, controls, training, and sustainment.
- Embed AI into standard operating processes and decision routines with appropriate human‑in‑the‑loop controls.
- Partner closely with plant leadership, supply chain, engineering, quality, EHS, reliability, and regional operations leaders to prioritize and deploy solutions.
Center of Excellence (COE) Leadership
- Build and lead an Operations AI enablement team (federated model) that accelerates delivery locally while aligning to enterprise standards and guardrails.
- Implement and ensure adherence to enterprise standards for governance, security, ethics, and lifecycle management for Operations AI use cases.
- Establish scalable delivery playbooks and reusable assets for Operations (e.g., intake templates, deployment runbooks, validation and monitoring checklists) aligned to approved enterprise platforms and architectures.
Change Leadership \& Capability Building
- Build AI literacy across Operations with role‑based curricula for operators, supervisors, engineers, planners, reliability, and quality teams.
- Lead end‑to‑end change management and adoption, working cross\-functionally with IT, HR and other functions to maintain consistency with enterprise programs.
- Define new or evolving roles in Operations (e.g., AI product owners, digital reliability leads); develop internal AI talent and communities of practice to sustain adoption.
Scope \& Key Interfaces
- Accountable for AI adoption and value realization across global Operations.
- Partners closely with DuPont’s AI/Digital teams for enterprise AI strategy, platforms, and Responsible AI guardrails, and with IT for architecture, integration, and cybersecurity.
- Serves as the primary interface with plant, regional, and business operations leaders.
- Owns business outcomes enabled by AI; does not own enterprise IT infrastructure.
Qualifications
- Bachelor’s degree in engineering, Operations, Computer Science, or related field; advanced degree (MBA/MS) preferred or equivalent experience.
- 10\+ years of leadership experience in manufacturing operations, and AI transformation in industrial environments.
- Proven experience translating operations into AI solutions that deliver value at scale.
- Deep understanding of discrete, process, or hybrid manufacturing environments and the realities of plant‑floor systems and data.
- Demonstrated ability to lead cross‑functional change across multiple sites or businesses; experience in diversified manufacturing strongly preferred.
Leadership Profile
- Strategic thinker with a strong execution bias and the ability to simplify complexity into scalable operating mechanisms.
- Highly credible with plant and operations leadership; capable of influencing from the frontline to the executive level.
- Strong business and financial acumen, including strategic planning, value realization, and portfolio prioritization.
- Excellent communicator who can translate effectively between operators and engineers, data and IT teams, and senior executives.
\#LI\-DP1
Join our Talent Community to stay connected with us!
DuPont is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability or any other protected class. If you need a reasonable accommodation to search or apply for a position, please visit our Accessibility Page for Contact Information.
DuPont offers a comprehensive pay and benefits package.
Salary Context
This $165K-$260K range is above the median 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 DuPont, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $165K to $260K.
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.
DuPont AI Hiring
DuPont has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wilmington, DE, US. Compensation range: $260K - $260K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.