Director, Applied AI Engineering - Life Sciences Markets
New
J
JobgetherLife sciences
Remote position open to candidates in the United States, Eastern, Central, or Pacific time zonesFull-TimeDirector
Salary not disclosed
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Job Details
- Required Skills
- AWSMachine LearningCI/CD
Requirements
- Demonstrated experience personally building and shipping production AI applications, with end-to-end technical ownership of complex systems and measurable business outcomes.
- Proven ability to lead cross-functional technical delivery while actively contributing to system design, modeling, coding, debugging, and deployment.
- Practical experience integrating machine learning models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation methodologies.
- Ability to assess model limitations and performance and select approaches based on available data and business requirements.
- Experience working directly with technical and non-technical client stakeholders, leading working sessions, and translating complex analysis into recommendations and conclusions.
- Hands-on experience with scalable data pipelines, automated testing, continuous integration and continuous delivery (CI/CD), and cloud infrastructure.
- AWS experience is preferred.
- Ability to manage application development and AI deployment for reliable, scalable production environments.
- Experience turning bespoke analytical projects, prototypes, or pilots into repeatable products adopted by multiple clients or teams.
- Background in life sciences, pharmaceutical analytics, or capital markets is advantageous but not mandatory.
- Strong technical judgment, ownership, collaboration, and communication skills, with the ability to balance hands-on engineering and delivery leadership.
Responsibilities
- Design, build, and deliver core AI services, applications, and workflows, contributing directly to implementation and technical problem-solving.
- Lead engineering squad execution, review code and architecture, establish technical standards, and coordinate priorities and dependencies.
- Identify life sciences market questions addressable with available data, prioritize opportunities, define success metrics, and develop technical and product roadmaps.
- Design and implement solutions combining machine learning models, LLMs, retrieval systems, and workflow automation.
- Establish automated testing, evaluation datasets, and monitoring; validate predictive models using point-in-time backtesting, baseline comparisons, and checks for data leakage and coverage bias.
- Move prototypes into production and own models, APIs, integrations, deployment, observability, and incident resolution.
- Develop reusable technical capabilities that scale across therapeutic areas, companies, and clients.
- Work with clients and subject matter experts to understand analytical workflows, facilitate working sessions, explain findings, and incorporate feedback into product priorities.
- Translate analytical findings into evidence-based conclusions for senior stakeholders and clients, including explaining data limitations.
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