JOB DESCRIPTION:
We are seeking a highly experienced AI Architect to lead the design and implementation of enterprise-scale AI/GenAI solutions for Life Sciences Commercial Operations. This role will focus on building scalable, secure, and business-driven AI platforms leveraging AWS Bedrock, Databricks (including Genie), and enterprise Copilot frameworks, along with deep integration of pharma commercial datasets (IQVIA, Symphony, patient, claims, Veeva CRM, and digital data).
The ideal candidate brings a strong combination of GenAI architecture expertise and Life Sciences commercial domain knowledge.
Key Responsibilities
AI / GenAI Architecture
- Design and implement GenAI-powered solutions using:
- AWS Bedrock (foundation models, agents, RAG architectures)
- Databricks Lakehouse & Genie (semantic layer, AI-assisted querying)
- Enterprise Copilot solutions integrated into business workflows
- Define reference architectures for:
- Conversational analytics platforms
- Intelligent commercial insights assistants
- Automated reporting and summarization
- Next-best-action and recommendation engines
Data & Platform Engineering
- Architect end-to-end data pipelines integrating:
- IQVIA data (Xponent, LAAD, etc.)
- Symphony datasets
- Patient and claims data (open & closed claims)
- Veeva CRM data
- Digital marketing and engagement platforms
- Enable data harmonization, semantic modeling, and AI-ready datasets
- Leverage Databricks (Delta Lake, Spark) and AWS-native services
Advanced Analytics & AI Use Cases
- Drive high-value use cases such as:
- HCP targeting, segmentation, and profiling
- Omnichannel engagement optimization
- Sales force effectiveness analytics
- Patient journey and adherence insights
- Market access and payer analytics
- Develop RAG-based solutions combining structured and unstructured data
- Build AI copilots for business users (brand teams, field force, market access)
Governance, Security, and Compliance
- Ensure compliance with:
- HIPAA and global data privacy regulations
- Implement Responsible AI practices
- Define data security, lineage, and access governance frameworks
Stakeholder Engagement
- Collaborate with:
- Commercial stakeholders (Sales, Marketing, Market Access)
- Data engineering and analytics teams
- Enterprise architecture teams
- Translate business needs into AI solution roadmaps and architectures
- Support solutioning, proposals, and client engagements
Required Qualifications
Experience
- 10 15+ years in data, analytics, or AI architecture
- 5+ years in Life Sciences Commercial domain
- Hands-on experience with:
- IQVIA, Symphony, Claims and Patient datasets
- Veeva CRM and commercial data ecosystems
Technical Skills
- AI/GenAI Platforms:
- AWS Bedrock (mandatory)
- Databricks (Lakehouse, ML, Genie)
- Enterprise Copilot frameworks
- Programming:
- AI Techniques:
- LLMs, RAG architectures, embeddings, vector search
- NLP and conversational AI systems
- Cloud:
- Strong expertise in AWS ecosystem
Architecture Expertise
- Enterprise AI architecture design
- Data modeling (semantic layers, ontologies)
- API-based integration and scalable microservices design
Preferred Qualifications
- Experience building AI copilots or conversational assistants at scale
- Familiarity with:
- Salesforce Data Cloud / Data 360
- Digital marketing platforms
- Experience with LLMOps / MLOps frameworks
- Knowledge of patient analytics and real-world data (RWD/RWE)
Leadership & Soft Skills
- Strong executive communication skills
- Ability to influence cross-functional stakeholders
- Experience leading architecture reviews and governance forums
- Outcome-driven mindset focused on business impact
Key Deliverables
- AI/GenAI architecture blueprints
- Production-grade AI solutions
- Commercial AI use case roadmap
- Reusable accelerators (RAG pipelines, copilots, semantic layers)
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