CTI MD Tech@Lilly – Senior Data Architect – AI & Agentic Solutions

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. About Lilly At Lilly, everything we do starts with patients. We unite caring with discovery to make life better for people around the world. Headquartered in Indianapolis, Indiana, our global team of over 50,000 employees work with urgency and purpose to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. We bring our best to this work because people depend on it. If you're driven by purpose and determined to make a meaningful difference for patients, we invite you to bring your skill and your commitment to Lilly. About Tech@Lilly At Lilly, technology is not a support function. It is how a global medicine company operates, innovates, and delivers. Lilly in Bengaluru builds the capabilities that make this possible — cloud platforms, AI systems, and automation at enterprise scale — all in service of a purpose that makes this technology work genuinely distinctive, from advancing drug discovery to enabling connected clinical trials to keeping a global medicine company running at the standard patients deserve. About the Organization The Clinical & Non-Clinical Data Organization at Eli Lilly and Company is responsible for the design, build, and operation of enterprise data platforms that power drug discovery, clinical development, and regulatory submissions. Data Hub is building a robust Data Strategy to make Lilly's Clinical and Non-Clinical data AI-ready and audit-ready, delivering scalable, governed, and reusable data products that accelerate how medicines reach patients. The data engineering organization sits at the intersection of science, technology, and patient impact — connecting Clinical and Non-Clinical data across the full chain, from ingestion to consumption. Path/Level: R5 (Senior Data Architect) Position Summary The Senior Data Architect (R5) is a hands-on leader who designs and personally builds AI and agentic solutions that operate at enterprise scale across Lilly's Clinical and Non-Clinical data domain — multi-agent systems, LLM-orchestrated pipelines, and retrieval/reasoning architectures built on governed, semantic data foundations. This is a builder role: the architect writes code, stands up agent frameworks, and ships production AI systems personally, not just specifications. This role is split 70% hands-on technical execution including coding and 30% strategy, and shapes the future technology landscape. A defining mandate of this role is Right Model, Right Task — routing every agentic and LLM workload to the model best suited to it on cost, latency, and accuracy grounds, integrated directly with Lilly's internal data platform so routing and reasoning are grounded in enterprise-native lineage and knowledge graphs rather than bespoke, disconnected metadata. Agentic and AI-assisted ways of working are the expected default across every design, analysis, and documentation activity — and this leader is the reference point for how the broader India team scales AI-native architecture. Key Responsibilities AI & Agentic Solution Architecture (Hands-On) Architect and personally build multi-agent and LLM-orchestrated solutions — planning/tool-calling agents, retrieval-augmented generation (RAG), and agent-to-agent workflows — for Clinical and Non-Clinical use cases. Design for scale from the start: agent orchestration, state management, concurrency, cost/latency budgets, and failure/retry handling across high-volume production workloads. Implement evaluation, guardrails, observability, and human-in-the-loop patterns so agentic systems are safe, auditable, and production-ready in a regulated environment. Right Model, Right Task – Platform-Integrated Routing, Lineage & Knowledge Graphs Design and implement a right-model-right-task routing layer that selects the optimal model — by size, provider, and fine-tuned vs. general-purpose — for each agentic task based on complexity, cost, latency, and accuracy requirements, rather than defaulting to one model for every job. Integrate directly with Lilly's internal data platform (Data Hub catalog, lineage, and metadata services) rather than building parallel metadata stores, so routing and agent reasoning are grounded in the enterprise's single source of truth. Build and maintain lineage-aware enterprise knowledge graphs sourced from the internal data platform, capturing data provenance, sensitivity, and domain context that ground both agentic reasoning and model-selection decisions. Use lineage and knowledge-graph context to enforce routing policy — for example, regulatory-sensitive Clinical data routes only to approved/validated models, while low-sensitivity exploratory tasks use lighter-weight, lower-cost models. Continuously benchmark and re-tune the model portfolio and routing rules as new models become available, avoiding both over-provisioning expensive frontier models and under-serving tasks that genuinely need them. AI-Native Data Engineering & Semantic Modeling Use AI-assisted and agentic tooling by default across the data lifecycle — pipeline generation, schema/ontology alignment, data-quality scoring, and documentation — not as an occasional accelerator. Design conceptual, logical, physical, and semantic models — ontologies, taxonomies, knowledge graphs — that ground both self-service analytics and agentic reasoning, built on and reconciled with the platform's native lineage. Build analytics-ready dimensional models and semantic/vector layers that power self-service BI and AI-driven consumption for scientists, statisticians, and business analysts. Automation & Team Reusability at Scale Build reusable agentic accelerators — agent templates, orchestration patterns, evaluation harnesses, model-routing configs — that the broader India team adopts directly to scale AI-native delivery. Automate lineage capture, schema-drift detection, and data/agent quality checks so the team spends more time on judgment calls, less on manual upkeep. Stand up and maintain the AI/agentic standards platform — the enterprise reference for how agentic solutions, model routing, and knowledge graphs are designed, governed, and delivered across Clinical and Non-Clinical squads. Governance & Responsible AI Implement role-based and attribute-based access control, encryption, and prompt/data-leakage safeguards for agentic systems operating on regulated data. Embed data quality, lineage, model/agent risk, and compliance requirements directly into every agentic design and routing decision. Collaboration & Stakeholder Engagement Partner with business SMEs, solution architects, platform/data-catalog teams, and engineering teams to translate Clinical and Non-Clinical needs into scaled agentic solutions. Communicate technical