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About This Role
Company:
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Oliver Wyman
Description:
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About Oliver Wyman
At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments.
As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annual revenue over $24 billion and more than 90,000 colleagues in 130 countries, Marsh helps build the confidence to thrive through the power of perspective.
For more information, visit oliverwyman.com , or follow us on LinkedIn and X .
Job Overview:
The AI Product Enablement Regional Manager leads the day\-to\-day execution of the AI Capability \& Enablement team within Quotient Inside in the Americas . Operating at the intersection of Product Strategy and Product Delivery, this role translates firm\-wide AI priorities into structured coaching, triage, experimentation, and scalable guidance . This role will be responsible for developing the acceleration strategy to adopt the firm's AI products across the region.
This role serves as a linchpin between strategic intent practical execution and hands\-on\-management — owning the operating rhythms, stakeholder coordination, and workflow management that enable AI usage to scale responsibly across Oliver Wyman. The Manager is both hands\-on and systems\-oriented, ensuring high\-quality enablement while identifying patterns, gaps, and improvement opportunities.
*Please Note: Oliver Wyman/Marsh operates a hybrid working policy of 60% in\-office attendance*
Key responsibilities :
Execution \& Operating Model Delivery
- Lead day\-to\-day operations of the AI Capability \& Enablement function, ensuring coaching, triage, experimentation, and standards\-setting activities run effectively
- Design and manage structured workflows for intake, routing, escalation, and feedback loops
- Track key performance indicators (e.g., adoption patterns, recurring use cases, response time, enablement effectiveness) and recommend course corrections
- Keep a pre\-built performance/analytics dashboard up to date
Community Engagement \& Triage
- Monitor OW channels and serve as a clearing house for responding to AI questions and actioning requests Assess inbound needs and route appropriately:
- Direct bugs or platform issues to formal feedback mechanisms
- Guide general AI usage questions to living exemplars, prompt libraries, or training materials
- Connect teams tackling similar use cases across Oliver Wyman and Marsh
- Maintain clarity and professionalism in high\-frequency interaction with senior and junior colleagues
Coaching \& AI Experimentation
- Provide AI coaching through office hours, working sessions, and 1:1 support with colle a gues who reach out via feedback channels
- Guide colleagues in translating ambiguous problems into AI\-solvable use cases
- Conducting hands\-on experimentation with AI tools, including:
+ Meta\-prompting refinement
+ Custom GPT configuration
+ Workflow design and prototyping
- For high\-value or repeatable problems, facilitate sessions that teach approach and thinking — not deliver final outputs
- Promote responsible, scalable, and effective AI usage across the region
Pattern Capture \& Institutional Learning
- Identify recurring patterns across user requests and use cases
- Synthesize insights into reusable artifacts (living exemplars, blog posts, short demos, structured guides)
- Contribute learning inputs to Product Strategy and the Director of AI Product Enablement in a helpful/usable format at a regular cadence
- When neede d, develop repeatable enablement assets
Stakeholder Coordination \& Project Leadership
- Lead multiple distinct enablement initiatives from planning through execution (e.g., rollout of new prompting standards, custom GPT playbooks, adoption pilots) within the region
- Coordinate cross\-functional contributors across Product Strategy, Design, Engineering, Training \& Comms, and platform teams
- Support roll\-out of new AI capabilities within the region
- Manage project timelines, risks, dependencies, and stakeholder communications
- Ensure regional execution remains aligned with enterprise AI priorities
Regional Leadership and Team Management
- Lead, coach, and develop a team of AI Product Enablement Analysts
- Foster a collaborative, high\-performing culture focused on continuous learning and exceptional user experience
- Allocate resources across product launches, enablement initiatives, and regional priorities
- Balance regional business needs while maintaining alignment with global AI strategy
- Responsible for establishing team priorities in alignment with regional and global priorities, operating rhythms, and managing performance expectations
Experience Required:
- 6–9 years of experience in AI enablement, digital delivery, product operations, project management, or related roles
- 3\+ years of people management experience, including managing and developing high\-performing teams
- Demonstrated experience leading cross\-functional initiatives with defined deliverables
- Hands\-on experience working with AI tools and workflows (e.g., custom GPTs, prompt engineering, AI assistants)
- Experience operating in professional services or complex, matrixed environments preferred
Skills \& Attributes:
- Structured operator with strong project management and facilitation skills including coaching
- AI experimentation fluency (meta\-prompting, workflow testing, custom GPT exploration)
- Strong stakeholder coordination and communication skills
- Analytical thinker capable of identifying patterns and synthesizing insights
- End\-user obsessed and committed to practical impact
- Comfortable balancing independence with alignment to strategic direction
The applicable base salary range for this role is $130,000 to $170,000\.
The base pay offered will be determined on factors such as experience, skills, training, location, certifications, education, and any applicable minimum wage requirements. Decisions will be determined on a case\-by\-case basis. In addition to the base salary, this position may be eligible for performance\-based incentives.
We are excited to offer a competitive total rewards package which includes health and welfare benefits, tuition assistance, 401K savings and other retirement programs as well as employee assistance programs.
Oliver Wyman is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit oliverwyman.com, or follow us on LinkedIn and X.
### Marsh is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, veteran status (including protected veterans), or any other characteristic protected by applicable law. If you have a need that requires accommodation, please let us know by contacting [email protected].
### Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office\-based teams will identify at least one “anchor day” per week on which their full team will be together in person.
Salary Context
This $130K-$170K range is below 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 Marsh, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $130K to $170K.
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.
Marsh AI Hiring
Marsh has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $170K - $170K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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