CTI MD Tech@Lilly – Senior Data Architect – AI & Agentic Solutions
Eli Lilly and CompanyMatch Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 pharma brand and Bangalore location boost applicant density despite senior, specialized requirements.
Requires regulated clinical-data, lineage, knowledge-graph, and LLM experience, limiting cross-industry transferability.
Multiple mandatory seniority, hands-on LLM/agentic, cloud, lineage, and regulated-data requirements make shortlisting highly selective.
Job Description
Structured overview of role & requirementsAbout This Role
Lead design and development of enterprise-scale AI and multi-agent systems in the Clinical and Non-Clinical data domain, including multi-agent orchestration, LLM pipelines, and retrieval/reasoning architectures.
Build and maintain an integrated model-routing system using enterprise data lineage and knowledge graphs to optimize AI model selection by cost, latency, and accuracy for regulated data environments.
Drive AI-native data engineering and semantic modeling practices, including building reusable agentic accelerators and automation tools to scale AI delivery across teams.
Minimum Requirements
Minimum 12+ years hands-on data architecture experience with 5+ years building and shipping AI/agentic production systems.
Bachelor's degree in Computer Science, Information Systems, or related discipline mandatory; Master's preferred but not mandatory.
Hands-on expertise in multi-agent AI systems, model orchestration/routing layers, semantic data modeling (ontologies, knowledge graphs) with regulated Clinical or Non-Clinical data.
Experience with cloud platforms (AWS), lakehouse platforms (Databricks or equivalent), and governance/compliance for regulated data (e.g., encryption, access control).
Ideal Candidate Profile
Senior-level technologist with deep expertise in AI/agentic architectures integrated with enterprise data platforms and compliance in regulated environments (pharma/life sciences).
Proven builder who codes and deploys production-grade multi-agent AI systems, not just design/specification.
Experienced in embedding AI-assisted methods and responsible AI governance into data lifecycle and team practices to operationalize secure, scalable agentic solutions.