Associate Director, AI Development Lead

Jazz Pharmaceuticals•US - Home-Based - PA, PA

2d•$153,600 - $230,400•Remote

About The Position

The Associate Director, AI Development Lead is a strategic technical leader responsible for advancing Jazz Pharmaceuticals' enterprise AI capability and driving measurable business value through artificial intelligence and machine learning. Reporting to the Director, Enterprise AI, this role will spend much of its time in delivery — designing systems, writing production-quality code, reviewing colleagues’ work, and guiding a small team of AI developers working across traditional data science (AI/ML) and modern Generative AI (GenAI) approaches. AI development at Jazz follows a hub-and-spoke operating model. Business functions (spokes) own use-case prioritization, stakeholder alignment, early-stage ideation, and some proof-of-concept development. The central Digital Enterprise Capabilities (DEC) team (hub) serves as an advisory partner at any stage and progressively takes on a hands-on development role as solutions mature — ensuring production-grade standards for data readiness, model quality, code maturity, and reliability. The business is accountable for deciding what gets built; DEC owns how it gets built and deployed. The role will also partner with the Director, Enterprise AI and the VP of Data, AI, and Research to help shape Jazz's enterprise AI strategy — operating model, governance, technology stack, best practices, and policies — and serve as a trusted subject-matter expert to teams across Jazz who are evaluating use cases, prioritizing initiatives, and vetting vendors. As the team and capability mature, the balance of the role will naturally evolve toward broader technical oversight and a larger share of strategic work, while continuing to direct the most important technical decisions.

Requirements

Demonstrated ability to lead, mentor, and be accountable for the technical work of others.
Demonstrated experience leading offshore or near shore development teams required.
Hands-on technical depth in building and shipping ML/GenAI solutions to production.
Proficiency in writing production-quality code and designing AI/ML architecture.
Strong understanding of core machine learning algorithms and statistical modeling techniques, model evaluation, and real-world deployment considerations.
Proficiency in one or more programming languages (e.g. Python, R) and ML frameworks (such as TensorFlow, PyTorch, scikit-learn).
Strong understanding of modern Generative AI concepts and approaches: LLMs, RAG, fine-tuning, agentic patterns, evaluation, and guardrails.
Significant hands-on experience with at least one major cloud platform (AWS, GCP, or Azure); AWS or GCP preferred.
Familiarity with AI governance and risk frameworks.
Excellent communication skills with ability to explain complex technical concepts in clear, non-technical terms, and a demonstrated ability to influence and guide stakeholders across various levels of an organization.
Bachelor’s degree or equivalent practical experience in a quantitative or technical field required.
5–7 years of relevant experience in AI, machine learning, or data science required.

Nice To Haves

Advanced degree or equivalent preferred.
10+ years preferred.
Experience in pharmaceutical, life sciences, or another regulated industry strongly preferred.
Experience evaluating and integrating third-party AI/ML solutions and vendor platforms strongly preferred.

Responsibilities

Build and ship end-to-end ML solutions — data ingestion, feature engineering, model training, evaluation, deployment, and monitoring — across supervised, unsupervised, and other classical/statistical learning paradigms.
Design and ship end-to-end GenAI solutions — including retrieval-augmented generation (RAG) systems, agentic workflows, vector stores, and integrations with foundation model APIs.
Across Gen AI and AI/ML, write production-quality code (Python or related languages/frameworks as needed), with attention to readability, modularity, scalability, performance, and maintainability.
Design and review architectures across Gen AI and AI/ML solutions that are secure, reusable, and rigorous.
Work with the Enterprise AI Architect to ensure your designs follow our architectural standards.
Debug, profile, and optimize solutions — including cost, latency, accuracy, and reliability trade-offs.
Support deployment and post-deployment operations, including CI/CD, observability, model monitoring, drift detection, and incident response.
Establish and continuously refine technical standards for development: reference architectures, design patterns, coding conventions, evaluation methodologies, and reusable components.
Lead a small team of AI developers across traditional ML and Generative AI, setting clear technical direction, priorities, and quality expectations.
Be accountable for the quality and outcomes of the team's work: solution design, code and model quality, documentation, and delivery.
Mentor team members through code & design review, both informal and formal feedback; build technical depth and ownership across the team.
Plan and sequence delivery work in partnership with stakeholders, balancing scope, technical risk, and timelines.
Help recruit, onboard, and grow AI talent as the team expands.
Serve as a go-to technical advisor to teams across Jazz, many of them non-technical, helping them: understand how AI works, how to ensure human-in-the-loop review and how to increase and optimize adoption of solutions while maintaining responsible and ethical use.
Evaluate AI use cases and proposed solutions for technical feasibility, data readiness, value, and risk.
Use expertise to support business in prioritizing AI initiatives based on business impact, technical feasibility, bandwidth constraints, solution complexity, and scalability.
Within agreed upon product priorities, decide resource allocation and development activities for the team.
Vet AI vendors and platforms in collaboration with our Enterprise AI Architect, as well as Infrastructure and Digital Security teams.
During such evaluations, this role will focus on solution fit for the use-case, overall solution practicality, and vendor knowledge of AI’s capabilities and limitations.
Support build-vs-buy decisions with a clear-eyed view of time-to-value, total cost of ownership, extensibility, and vendor risk.
Partner with the Director, Enterprise AI and the VP of Data, AI, and Research to help shape and execute Jazz's enterprise AI strategy, including operating model, governance, technology stack, best practices, and policies.
Contribute to the maturation of the AI capability through reference architectures, MLOps practices, evaluation frameworks, and reusable components that make future delivery faster and more reliable.
Help define and apply Responsible AI practices — explainability, evaluation, privacy, security, and risk management — across the work the team delivers and supports.
Collaborate with Data Engineering, Enterprise Architecture, Infrastructure, InfoSec, and Compliance to ensure AI solutions meet enterprise standards.
Build credibility quickly with senior technical and business stakeholders through effective communication and deep expertise.