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Location: Chicago Other locations: Anywhere in Country Salary: Competitive Date: Oct 7, 2026 Job description
Requisition ID: 1748978 Location: Anywhere in Country
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We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
Client-facing Life Sciences manufacturing transformation leader accountable for shaping and delivering technology-enabled programs that improve manufacturing performance, quality, compliance, and supply reliability. The role combines regulated plant-floor expertise with data, AI, automation, and disciplined program delivery.
The Opportunity
As a Senior Manager in Supply Chain Manufacturing, you will lead transformation programs for pharmaceutical, biotechnology, and medical-device manufacturers, from defining the operational problem and value case through solution design, implementation, adoption, stabilization, and measurable results. You will work across manufacturing, quality, laboratory, engineering, supply chain, and technology teams to connect plant operations with enterprise systems and data.
This is a techno-functional delivery role. You will need the credibility to redesign manufacturing processes and operating models, and the fluency to direct teams delivering manufacturing execution, automation, integration, data, and AI solutions. You will remain accountable for delivery quality, risk, client relationships, and outcomes in regulated environments. “The Life Sciences focus reflects the practice’s emphasis on connected manufacturing and quality workflows across process, batch, and discrete operations, including electronic batch records, electronic device history records (eDHRs), traceability, and integration across plant and enterprise systems.”
Your key responsibilities
In this role, you will lead complex client engagements while balancing operational improvement, technology delivery, quality requirements, and commercial commitments. You will:
- Lead end-to-end Life Sciences manufacturing transformations, from assessment and mobilization through design, deployment, stabilization, and value realization.
- Build trusted relationships with senior manufacturing, quality, MS&T, engineering, supply chain, digital, and IT/OT stakeholders; establish governance and drive timely decisions.
- Translate priorities such as yield, throughput, right-first-time execution, deviation reduction, batch-cycle time, asset reliability, and supply continuity into practical workplans and outcome measures.
- Direct multidisciplinary teams across manufacturing process design, MES/MOM, automation, data engineering, AI, architecture, integration, quality, change management, testing, and deployment.
- Lead solution and process design for batch and discrete execution, electronic batch records, electronic device history records (eDHRs), recipes and routings, material and product genealogy, review by exception, maintenance, quality workflows, and plant performance management.
- Guide integration across MES/MOM, ERP, LIMS, QMS, historians, SCADA, DCS/PLC, asset-management systems, and relevant cloud or edge platforms.
- Establish trusted manufacturing data foundations through appropriate data models, asset and process context, master data, metadata, lineage, governance, and secure IT/OT connectivity.
- Shape analytics and AI use cases, such as process monitoring, predictive maintenance, deviation investigation support, digital twins, and decision support; assess their value, data readiness, workflow fit, and risks before scaling.
- Partner with client quality and validation teams to incorporate applicable GxP controls, data integrity, electronic-record requirements, change control, and risk-based assurance into delivery plans. The applicable controls will depend on the product, system, and intended use.
- Manage scope, schedule, budget, staffing, dependencies, quality, risks, issues, and contractual commitments; guide testing, cutover, training, adoption, and operational handover.
- Connect digital solutions to daily management, operational excellence, workforce capability building, and sustained process improvement.
- Ability to create clarity and alignment across distributed teams, motivate people through complex delivery challenges, and maintain accountability without becoming the bottleneck for decisions.
- People leadership and talent-development capability, including coaching, delegation, performance feedback, conflict resolution, succession planning, and building teams with the right mix of functional, technical, and industry skills.
- Develop Managers and delivery teams, contribute reusable methods and lessons learned, and identify appropriate follow-on opportunities with account teams.
Skills and attributes for success
Success in this role requires the ability to connect regulated manufacturing realities with scalable technology delivery. You will bring:
- Strong knowledge of Life Sciences manufacturing processes and the relationship between production, quality, laboratory operations, engineering, and supply.
- Techno-functional leadership that earns the confidence of plant teams while providing clear direction to architects, engineers, developers, data scientists, and cybersecurity specialists.
- Working knowledge of industrial data and integration patterns, including OPC UA (IEC 62541), MQTT/Sparkplug, APIs, historians, asset and batch contextualization, and unified-namespace approaches; able to evaluate how these patterns support secure, reliable data exchange across OT and enterprise systems.
- Sound judgment about where analytics or AI can improve a workflow, what evidence is needed to support its use, and where human review and quality oversight must remain.
- Executive communication, structured problem-solving, facilitation, negotiation, and the ability to make decisions under ambiguity.
- Commercial and delivery discipline across estimates, staffing, engagement economics, change control, risk management, and value realization.
To qualify for the role, you must have:
- A bachelor’s degree in engineering, life sciences, manufacturing, supply chain, information systems, business, or a related discipline.
- No less than 10+ years of relevant experience spanning Life Sciences manufacturing, manufacturing technology, operational excellence, industrial data and AI, technology consulting, or a related field.
- Experience leading complex manufacturing transformation or technology-enabled improvement initiatives in a pharmaceutical, biotechnology, medical-device, or comparably regulated environment.
- Experience delivering client-facing programs through design, implementation, deployment, and stabilization, with accountability for governance, resources, quality, financial performance, and risk.
- Experience leading senior stakeholders and multidisciplinary teams across business, functional, data, and technology workstreams.
- Experience translating manufacturing requirements into process, system, data, integration, security, and technical requirements, with traceability through testing and deployment.
- Practical understanding of GxP manufacturing and the quality, documentation, change-control, and data-integrity considerations relevant to digital solutions.
- Ability to travel as needed based on business and role requirements. Travel can range from 80-100%.
Ideally, you’ll also have:
- Experience with drug substance, drug product, biologics, sterile manufacturing, medical devices, or clinical-to-commercial scale-up.
- Experience leading multi-site deployments, greenfield or brownfield plant modernization, or manufacturing technology-transfer initiatives.
- Implementation experience with one or more MES/MOM platforms, electronic batch records and eDHRs, recipe management, connected-worker tools, industrial analytics, or manufacturing data platforms.
- Familiarity with ISA-88/IEC 61512, ISA-95/IEC 62264, OPC UA/IEC 62541, and OT cybersecurity practices informed by ISA/IEC 62443 and NIST SP 800-82. Exposure to IEC 61131-3, IEC 61499, CESMII i3X, O-PAS, or SiLA 2 is a plus where relevant to the client environment.
- Familiarity with Process Analytical Technology (PAT) and continuous-improvement practices, and their application to manufacturing analytics, process performance, quality, and operational decision-making.
- Experience with computer system validation or risk-based computer software assurance, as applicable to the systems and regulated products involved.
- Experience applying AI or advanced analytics to manufacturing or quality workflows with appropriate model governance, monitoring, and human oversight.
- A master’s degree or relevant program-management, operational-excellence, cloud, data, or manufacturing-technology certification.
What we look for
We are looking for a Senior Manager who can move confidently between the plant floor and the executive discussion. The successful candidate will combine Life Sciences manufacturing credibility, quality-minded judgment, technology and data fluency, commercial awareness, and hands-on delivery leadership to produce improvements that clients can operate and sustain.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- The salary range for this job is: + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $180,500 to $374,800 + Bay Area California offices – $188,100 to $390,500 + All other offices locations in the US, including Sacramento – $150,400 to $343,600
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
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All in to shape the future with confidence.
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Salary Context
This $150K-$374K range is above the 75th percentile 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 EY, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $150K to $374K.
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.
EY AI Hiring
EY has 70 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, AI Safety. Positions span Stamford, CT, Hoboken, NJ, New Brunswick, NJ. Compensation range: $142K - $374K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national 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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