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Apply Now →About This Role
At Eisai, satisfying unmet medical needs and increasing the benefits healthcare provides to patients, their families, and caregivers is Eisai’s human health care (hhc) mission. We’re a growing pharmaceutical company that is breaking through in neurology and oncology, with a strong emphasis on research and development. Our history includes the development of many innovative medicines, notably the discovery of the world's most widely\-used treatment for Alzheimer’s disease. As we continue to expand, we are seeking highly\-motivated individuals who want to work in a fast\-paced environment and make a difference. If this is your profile, we want to hear from you.
The Regional AI Delivery Lead (Associate Director) is accountable for the end\-to\-end delivery, adoption, and performance of AI solutions within the Americas and EMEA regions. The role owns the regional AI portfolio, including identifying, prioritizing, and sequencing high\-value use cases aligned to business and enterprise strategy.
Acting as a “player\-coach,” the individual leads the execution of AI solutions while guiding regional contributors and driving alignment with business stakeholders to ensure measurable outcomes and value realization. The role operates as the primary regional point of contact for AI delivery, influencing prioritization decisions, managing delivery capacity, and ensuring solutions meet business needs.
The Regional AI Delivery Lead is accountable for ensuring that all solutions in\-region operate effectively in production and comply with enterprise governance, security, and regulatory standards. Working within a global, matrixed delivery model, the role partners with Global AI Operations, Data \& Analytics, and AI Governance teams to ensure alignment with enterprise platforms, standards, and priorities while adapting solutions to meet regional requirements.
The role also provides critical oversight of third\-party delivery within the region and contributes to cross\-regional collaboration by sharing best practices, scaling successful solutions, and providing feedback to improve global frameworks and capabilities.Functional Responsibilities:
1\. Regional AI Delivery \& Portfolio Ownership
- Own the regional AI delivery portfolio, including identifying, prioritizing, and sequencing high\-value use cases aligned to enterprise priorities.
- Translate business needs into scalable AI solutions using platforms such as Microsoft Copilot, ChatGPT Enterprise, and Databricks.
- Provide structured input into global prioritization and funding discussions.
- Solves complex regional challenges with incomplete information.
2\. Solution Development \& Execution (Player\-Coach Model)
- Lead end\-to\-end delivery of AI solutions from concept through deployment.
- Develop prototypes, agents, and workflows where appropriate, while guiding SMEs and regional contributors.
- Provide hands\-on support for complex or high\-priority use cases.
3\. Regional Accountability for AI Operations \& Governance
- Serve as the accountable leader for ensuring AI solutions are running effectively within the region.
- Oversee AI Operations to ensure smooth handoffs into production, ongoing support, operational performance monitoring, and issue resolution.
- Partner with regional AI Governance to address region\-specific regulatory requirements.
4\. Third\-Party Delivery Governance \& Oversight
- Ensure all AI deliverables, within the region, meet enterprise quality, security, and architectural standards through review and governance mechanisms.
- Act as the escalation point for delivery quality, consistency, and compliance issues.
- Provide direct oversight for third\-party consultants engaged through the AI Enablement function.
- Manage regional delivery capacity, including vendor utilization and resource allocation.
5\. Stakeholder Engagement \& Value Realization
- Act as the primary point of contact for AI delivery within the region.
- Facilitate prioritization discussions with functional leaders and translate demand into executable roadmaps.
- Align regional portfolio to business priorities and articulates trade\-offs
- Ensure deployed solutions drive measurable productivity gains and business outcomes.
- Coach SMEs and business partners on AI solution development and adoption.
6\. Cross\-Regional Collaboration \& Scaling
- Influence and adapt global AI architecture standards, delivery frameworks, and capability development.
- Collaborate with other Regional AI Delivery Leads to share reusable solutions and best practices adapting for local needs.
- Provide feedback to global teams on gaps in frameworks, tools, or standards.
- Operate effectively within a matrixed global delivery model.
Qualifications:
- Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Data Science, Business, Engineering).
- 8\+ years of experience in technology delivery, digital transformation, or product/program management.
- 2–3\+ years delivering AI, automation, or advanced analytics solutions in an enterprise environment.
- Experience managing third\-party vendors, consultants, or system integrators
- Hands\-on experience with AI platforms (e.g., Microsoft Copilot, ChatGPT Enterprise, Databricks).
- Strong stakeholder management skills with the ability to influence across a matrixed organization.
- Experience operating in global delivery models with distributed teams.
- Must be able to work hybrid in our Nutley, NJ office 3 days per week (Tuesday, Wednesday and Thursday each week).
Key Success Metrics:
- Number and quality of AI use cases successfully delivered within their regions.
- Adoption and sustained usage of deployed AI solutions.
- Measurable business value (e.g., productivity gains, cost savings, cycle time improvements).
- Speed and efficiency of delivery from intake to deployment.
- Stability and performance of AI solutions in\-region.
- Timely identification and resolution of production issues.
- Adherence of all solutions (internal and third\-party) to enterprise standards.
- Quality and consistency of third\-party delivered solutions.
- Reuse of AI solutions across functions and regions.
- Reduction in rework due to standards enforcement.
- Effective transition of solutions to Global AI Operations with minimal issues.
Eisai Salary Transparency Language:
The annual base salary range for the Associate Director, Regional AI Delivery Lead is from :$173,700\-$228,000
Under current guidelines, this position is eligible to participate in : Eisai Inc. Annual Incentive Plan \& Eisai Inc. Long Term Incentive Plan.
Final pay determinations will depend on various factors including but not limited to experience level, education, knowledge, and skills.
Employees are eligible to participate in Company employee benefit programs. For additional information on Company employee benefits programs, visit https://careers.eisai.com/us/en/compensation\-and\-benefits.
Certain other benefits may be available for this position, please discuss any questions with your recruiter.
Eisai is an equal opportunity employer and as such, is committed in policy and in practice to recruit, hire, train, and promote in all job qualifications without regard to race, color, religion, gender, age, national origin, citizenship status, marital status, sexual orientation, gender identity, disability or veteran status. Similarly, considering the need for reasonable accommodations, Eisai prohibits discrimination against persons because of disability, including disabled veterans.
Eisai Inc. participates in E\-Verify. E\-Verify is an Internet based system operated by the Department of Homeland Security in partnership with the Social Security Administration that allows participating employers to electronically verify the employment eligibility of all new hires in the United States. Please click on the following link for more information:
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Salary Context
This $173K-$228K 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 Eisai, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($200K) sits 8% below the category median. Disclosed range: $173K to $228K.
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.
Eisai AI Hiring
Eisai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nutley, NJ, US. Compensation range: $228K - $228K.
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
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