Accountabilities:

Hands-On AI Development: Personally design, build, and deliver core AI services, applications, and workflows, taking ownership of critical implementation tasks and resolving technical challenges alongside engineers, data scientists, and domain specialists.

Technical Leadership and Team Management: Lead the engineering team and oversee overall squad execution. Establish technical and implementation standards through practical contributions, review code and architecture, and coordinate priorities, dependencies, and delivery.

Strategy and Roadmap Development: Identify high-value life sciences market questions that can be addressed using available data. Prioritize opportunities, define success metrics, and translate business needs into a focused technical and product roadmap.

AI Architecture and Implementation: Design and implement solutions that combine machine learning models, large language models (LLMs), retrieval systems, and workflow automation. Make informed trade-offs involving model quality, reliability, latency, scalability, and cost.

Engineering Quality and Model Evaluation: Establish automated testing, evaluation datasets, and monitoring processes to ensure the quality and reliability of AI outputs and applications. Maintain rigorous predictive model validation through point-in-time backtesting, baseline comparisons, and checks for data leakage and coverage bias.

Production Deployment and Ownership: Move prototypes into production and take responsibility for models, APIs, integrations, deployment, observability, and incident resolution. Develop reusable technical capabilities that can scale across therapeutic areas, companies, and clients.

Client and Domain Expert Collaboration: Work directly with clients and subject matter experts to understand analytical workflows, facilitate working sessions, explain findings, challenge assumptions, and incorporate feedback into product priorities.

Business and Technical Alignment: Translate complex analytical findings into clear, evidence-based conclusions for senior stakeholders and clients, including communicating limitations where the available data does not support a particular conclusion.

Requirements

Production AI Engineering: Demonstrated experience personally building and shipping production AI applications, with end-to-end technical ownership of complex systems and measurable business outcomes.

Technical Leadership: Proven ability to lead cross-functional technical delivery while remaining actively involved in system design, modeling, coding, debugging, and deployment.

AI and LLM Expertise: Deep practical experience integrating machine learning models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation methodologies.

Model Assessment: Ability to understand model limitations, evaluate performance, and select appropriate approaches based on the available data and business requirements.

Client-Facing Communication: Experience working directly with technical and non-technical client stakeholders, leading collaborative working sessions, and translating complex analysis into clear recommendations and conclusions.

Data and Software Engineering: Strong hands-on experience with scalable data pipelines, automated testing, continuous integration and continuous delivery (CI/CD), and cloud infrastructure. Experience with AWS is preferred.

AI Deployment and Infrastructure: Ability to manage both application development and AI deployment, ensuring solutions are reliable, scalable, and suitable for production environments.

Product Development: Experience transforming bespoke analytical projects, prototypes, or pilot initiatives into repeatable products adopted by multiple clients or teams.

Industry Knowledge: Background in life sciences, pharmaceutical analytics, or capital markets is highly advantageous but not mandatory.

Leadership and Problem-Solving: Strong technical judgment, ownership, collaboration, and communication skills, with the ability to balance hands-on engineering responsibilities and broader delivery leadership.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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