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Executive Director of Applied AI

Novartis
Posted a day ago, valid for 22 days
Location

East Hanover, PA, US

Salary

$225,400 - $418,600 per year

Contract type

Full Time

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Sonic Summary

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  • The Executive Director, Applied AI Lead position at Novartis is located in Cambridge or East Hanover and requires at least 15 years of progressive experience in technology leadership and enterprise AI transformation.
  • The role involves defining and leading the applied AI deployment strategy, overseeing a globally distributed team, and ensuring the delivery of AI products that create measurable business value.
  • Candidates should have deep expertise in applied AI delivery, strong software engineering skills, and hands-on proficiency with modern AI engineering stacks.
  • The salary for this position ranges from $225,400 to $418,600 per year, with additional performance-based incentives and a comprehensive benefits package.
  • This position offers a unique opportunity to shape AI initiatives in a global enterprise that supports healthcare systems and patient care.

Job Description Summary

Location: Cambridge or East Hanover
#LI-Hybrid 3 days/week in office
Internal Job Title: The Executive Director, Applied AI Lead

Novartis is making a significant long-term investment in becoming an AI-first organization, with sponsorship at the highest levels of the company. This is a rare opportunity to shape how generative AI and agentic systems are engineered, deployed and adopted across a global enterprise whose work ultimately supports patients and healthcare systems worldwide. As Executive Director of Applied AI, you will combine technical depth, product thinking and enterprise influence to turn high-value business challenges into secure, scalable AI products and intelligent workflows. You will lead a small, globally distributed team of applied AI engineers and work directly with senior business and technology leaders to deliver measurable value at scale.


 

Job Description

Key Responsibilities 

  • Define and lead the applied AI deployment strategy, execution roadmap and business integration model across enterprise business units. 

  • Act as a forward-deployed AI engineering leader, partnering directly with business teams to translate strategic priorities into deployable products, intelligent workflows and redesigned processes. 

  • Lead technical solution architecture for complex AI deployments, making hands-on decisions on application architecture, system integration patterns, model orchestration, APIs, security controls and production infrastructure. 

  • Lead end-to-end implementation, customization, integration and scaling of generative AI, agentic systems and workflow automation solutions in production environments. 

  • Build, lead and coach a small, globally distributed team of forward deployed AI engineers, establishing engineering excellence through technical review processes, coding standards and career development while actively guiding delivery on critical engagements. 

  • Establish delivery frameworks and engineering standards that support scalability, reliability, security, maintainability and sustained operational adoption. 

  • Create reusable AI services, accelerators, evaluation frameworks, blueprints and integration patterns that reduce time to value across business domains. 

  • Partner with data, infrastructure, information security, architecture, compliance and business teams to align solutions with enterprise priorities and regulated-environment requirements. 

  • Track solution performance, adoption and business value through monitoring, feedback loops, user analysis and continuous service optimization. 

  • Guide modernization of existing systems and processes so they can integrate with agentic AI products, applied AI models and enterprise technology platforms. 

  • Communicate business impact, technical transformation vision, delivery progress, risks and investment needs to senior executives and key stakeholders. 

Essential Requirements 

  • Bachelor's degree in computer science, engineering, data science or a related field. 

  • At least 15 years of progressive experience in technology leadership, enterprise AI transformation or complex solution delivery, including leadership of senior technical and cross-functional teams. 

  • Deep, hands-on expertise in applied AI delivery, including taking machine learning, generative AI and agentic solutions from concept through production and enterprise-scale adoption. 

  • Strong software and product engineering grounding, with experience integrating AI into complex enterprise platforms, data environments and operational workflows. 

  • Hands-on proficiency with modern AI engineering stacks, including Python, cloud platforms (e.g., AWS), LLM orchestration frameworks, vector databases, MLOps/LLMOps and software delivery practices. 

  • Demonstrated success defining and scaling enterprise-level AI initiatives, with measurable business value, automation or performance outcomes. 

  • Experience leading forward deployed, solutions engineering or technical consulting teams, translating ambiguous business problems into production-grade software systems in enterprise environments. 

  • Consulting or professional-services experience partnering with senior technology and business leaders on enterprise transformation, operating models or technology strategy. 

  • Proven people leadership and organizational development capability, including building high-performing teams, developing leaders and establishing effective governance. 

  • Strong executive communication and cross-functional influence skills, with the ability to align priorities, investment decisions and delivery across a complex matrixed organization. 

Desirable Requirements 

  • Master of Science, engineering postgraduate degree or MBA. 

  • Experience delivering AI solutions in healthcare, life sciences or another complex regulated environment, or prior zero-to-one startup experience. 

Benefits & Rewards 

The salary for this position is expected to range between $225,400.00 - $418,600.00 per year. 

The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors. 

Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards. 

US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves. 

 

To learn more about the culture, rewards and benefits we offer our people click here. 


 

EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. 


 

Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.


 

Salary Range

$225,400.00 - $418,600.00


 

Skills Desired

Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Stakeholder Engagement, Statistical Analysis, Time Series Analysis



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