Onsite Program & Technical Lead- Data, Cloud & Agentic AI (Commercial Pharma)

Required Skills & Experience

Technical

Domain

Delivery & Leadership

Behavioural

Desirable

Success Measures

Technical

• Data & Cloud: Strong expertise in AWS, Databricks, data engineering and BI.
• Agentic/Enterprise AI: Strong knowledge of agent design, multi-agent orchestration, routing, evaluation frameworks and guardrails.
• Responsible AI: Practical experience applying validation, explainability, security, monitoring and documentation standards.
• Power Platform: Working knowledge of Power Apps and Copilot Studio, with willingness to prototype and perform hands-on technical work.

Domain

• Good understanding of Commercial Pharma data and processes (eg sales and marketing analytics, field force, market access, patient and brand data).

Delivery & Leadership

• Proven experience running large, multi-workstream programs with onsite/offshore delivery models.
• Experience leading governance forums and reporting to senior business and technology stakeholders.
• Track record of owning delivery through to production with measurable business outcomes.
• Experience working with enterprise architecture, security and platform teams, including ARB processes.

Behavioural

• Credible, hands-on technologist who can challenge engineering decisions and also explain trade-offs to business leaders.
• Strong communication, influencing and relationship-building skills.
• Proactive, outcome-driven, comfortable with ambiguity and innovation.

Desirable

• AWS and/or Databricks certifications.
• Experience with GenAI and LLM platforms in regulated (life sciences) environments.
• Familiarity with GxP, data privacy and compliance considerations in pharma.
• Product management or product-centric delivery experience.

Success Measures

• Delivery against milestones and effective risk and dependency management.
• Customer and stakeholder satisfaction and sentiment.
• Adoption and demonstrable business value of AI, analytics and automation initiatives.
• Reuse of patterns and accelerators across initiatives.
• Quality, reliability and compliance of AI solutions in production.