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About This Role
The Director Applied AI Solutions – NA Commercial is responsible for defining and leading the North America Commercial AI strategy, portfolio, and roadmap to drive business performance, customer engagement, operational excellence, and innovation. This role identifies and prioritizes high\-value opportunities across patient finding, HCP engagement, next\-best\-action, insights generation, and commercial operations, translating business needs into scalable, compliant AI\-enabled capabilities that deliver measurable value.
Serving as the business owner and strategic leader for Commercial AI solutions, the incumbent partners across Commercial, Data Science, Digital/IT, Legal, Compliance, Privacy, and enterprise platform teams to drive governance, investment decisions, solution adoption, and value realization. The role provides thought leadership on emerging AI technologies, influences senior stakeholders, and ensures AI capabilities are aligned with commercial priorities, enterprise standards, and long\-term growth objectives.
WHAT \- Main Responsibilities
Build and deliver NA Commercial AI solutions (primary focus)
- Design and build AI\-powered workflows, agents, and applications for prioritized NA Commercial use cases, including patient finding, patient identification, HCP targeting, field insights, next\-best\-action, and content and insights automation.
- Translate Commercial business problems into technical solution designs and executable delivery plans.
- Build and test LLM, agentic AI, retrieval\-augmented generation (RAG), and predictive AI solutions using approved Ipsen data and technology environments.
- Convert analytics, models, and prototypes into scalable, compliant, and business\-ready solutions that can be adopted by NA Commercial teams.
- Develop evaluation frameworks for accuracy, grounding, hallucination risk, reliability, business KPIs, cost, and performance; iterate based on measured results.
- Partner with the Data Science team to productionize models and prototypes and with IT/platform teams to deploy and support solutions.
Commercial use\-case and product ownership
- Partner with NA Commercial Operations, Brand, Field, Patient Services, Value \& Access, and Analytics teams to identify and prioritize AI opportunities.
- Maintain the roadmap and backlog for assigned NA Commercial AI solutions.
- Define business requirements, user experience, adoption plans, and value measurement for solutions in scope.
- Ensure solutions are integrated into Commercial workflows rather than delivered only as technical prototypes.
- Gather business feedback and prioritize enhancements based on adoption and measurable impact.
AI solution and vendor evaluation
- Conduct structured evaluations and pilots of AI tools relevant to NA Commercial use cases.
- Define business, technical, compliance, cost, and scalability criteria; build test approaches and benchmark vendor claims.
- Evaluate the suitability of approved platform AI capabilities, including Snowflake Cortex and Salesforce/Agentforce, for specific NA Commercial use cases.
- Make evidence\-based build\-versus\-buy recommendations in partnership with IT, Procurement, Legal, Compliance, Privacy, and relevant platform owners.
- Ensure vendor solutions meet defined Commercial outcomes and align with approved architecture, security, privacy, and data\-handling requirements.
Solution lifecycle, quality, and responsible AI
- Define and manage the lifecycle for the NA Commercial AI solutions in scope, including deployment, versioning, evaluation, monitoring, and ongoing enhancement.
- Establish fit\-for\-purpose testing for output quality, accuracy, grounding, hallucination risk, bias, reliability, cost, latency, and business performance.
- Apply Ipsen privacy, compliance, security, and responsible AI requirements to NA Commercial solutions.
- Partner with IT and platform teams on deployment controls, access management, monitoring, and production support.
- Document solution logic, intended use, limitations, controls, and performance.
- Contribute NA Commercial requirements, reusable components, and learnings to broader Ipsen AI standards and knowledge sharing.
HOW \- Knowledge \& Experience
Knowledge \& Experience (essential):
- Significant experience (10\+ years) in software, machine learning, applied AI, or AI solution development, including a track record of delivering production solutions with measurable business impact.
- Hands\-on experience (typically 2\+ years) building and deploying LLM/GenAI applications in production, including agentic workflows, RAG, grounding, and evaluation, delivered through frameworks or directly on foundation\-model APIs.
- Advanced hands\-on proficiency in Python and SQL; experience building solutions using approved cloud AI/ML services.
- Experience managing deployed AI solutions through monitoring, versioning, evaluation, quality controls, cost management, and CI/CD practices.
- Demonstrated ability to translate ambiguous business needs into technical solutions and partner effectively with Commercial, Data Science, and technology stakeholders.
- Experience owning an AI solution from use\-case definition and development through adoption, performance measurement, and enhancement.
- Advanced hands\-on proficiency in Python and SQL; experience with APIs, integration patterns, containerization, and Git\-based workflows.
- Hands\-on development of agentic AI and LLM applications, including orchestration, tool/function calling, RAG, prompt engineering, grounding, and evaluation.
- Experience using frameworks such as LangGraph or LlamaIndex, or building directly on foundation\-model APIs such as OpenAI, Anthropic, AWS Bedrock, or Azure OpenAI.
- Experience building solutions with approved cloud AI/ML services; familiarity with vector databases and AI/LLM observability tooling such as LangSmith, Langfuse, MLflow, or equivalent.
- Working knowledge of AI capabilities in NA data platforms, including Snowflake Cortex and Salesforce/Agentforce.
- Practical experience with technical delivery controls, including model/prompt/agent versioning, CI/CD, monitoring, quality management, cost management, and performance optimization.
Knowledge \& Experience (preferred):
- NA Commercial pharma/biotech experience, ideally with use cases such as patient finding, HCP targeting, field insights, next\-best\-action, or Commercial workflow automation.
- Familiarity with healthcare data, including medical and pharmacy claims such as IQVIA or Komodo, EHR, specialty pharmacy, and coding systems such as ICD, CPT, and NDC.
- Experience evaluating AI vendors and approved data\-platform capabilities, including Snowflake Cortex and Salesforce/Agentforce, for Commercial use cases.
- Awareness of privacy, compliance, and responsible AI considerations in healthcare, including HIPAA, PHI/PII handling, and promotional review.
Education / Certifications (essential):
- BA/BS in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.
Education / Certifications (preferred):
- Advanced degree (MS/PhD) in a relevant field; cloud or machine learning certifications.
Language(s) (essential):
- Fluent in English.
Compensation
The annual base salary range for this position is $177,000 \- $260,000
This job is eligible to participate in our short\-term incentives program. At Ipsen we are proud to offer a comprehensive employee benefits package, including 401(k) with company contributions, group medical, dental and vision coverage, life and disability insurance, short\- and long\-term disability insurance, as well as flexible spending accounts. Ipsen also provides parental leave, paid time off, a discretionary winter shutdown, well\-being allowance, commuter benefits, and much more.
The pay range displayed above is the range of base pay compensation within which Ipsen expects to pay for this role at the time of this posting. Individual compensation within this range depends on a variety of factors, including, but not limited to, prior education and experience, job\-related knowledge and demonstrated skills.
We are committed to creating a workplace where everyone feels heard, valued, and supported; where we embrace “The Real Us”. The value we place on different perspectives and experiences drives our commitment to inclusion and equal opportunities. When we include diverse ways of thinking, we make more thoughtful decisions and discover more innovative solutions. Together we strive to better understand the communities we serve. This means we also want to help you perform at your best when applying for a role with us. If you require any adjustments or support during the application process, please let the recruitment team know. This information will be handled with care and will not affect the outcome of your application. Ipsen is an equal opportunity employer that strictly prohibits unlawful discrimination. We recruit, employ, train, compensate, and promote without regard to an individual’s race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, mental/physical disability, medical condition, marital status, veteran status, or any other characteristic protected by law.
Salary Context
This $177K-$260K range is above the 75th percentile 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 IPSEN, 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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $177K to $260K.
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.
IPSEN AI Hiring
IPSEN has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $260K - $260K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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