Technical Lead Architect

Overview

Technical Lead Architect for Lilly's GSS Tech team, setting the technical direction for clinical trial statistics platforms and building them. This is a technical leadership role that stays hands-on, roughly 40% building and 60% architecture, strategy and leadership.

What you'll do

• Define the target-state architecture, AI and agentic strategy and roadmap for GSS, including where automation should replace manual steps.
• Turn business priorities into a prioritized investment plan.
• Establish reusable reference architectures, secure and compliant patterns and engineering standards through shipped implementations.
• Lead architecture reviews, evaluate emerging technologies and act as the technical authority for architecture decisions, in partnership with enterprise architecture, security and platform teams.
• Own technical delivery from scoping through release, managing risks, dependencies and timelines.
• Oversee vendor and partner contributions for quality, security and standards.
• Partner with biostatistics, statistical programming, SDN&A and product leaders to align priorities and communicate decisions, trade-offs and delivery status.
• Coach engineers and emerging technical leads through code reviews, pairing and architecture spikes, and represent GSS in enterprise architecture forums.
• Automate clinical statistics workflows from statistical analysis plan (SAP) to submission, including specifications, ADaM and TFL programming in R and SAS, QC and CDISC deliverables (SDTM, ADaM, define.xml), using AI agents with statisticians and programmers reviewing results.
• Architect the platform where statisticians and programmers work: reproducible, version-controlled, containerized R and SAS environments, with end-to-end lineage from specifications to outputs.
• Build cloud-native applications and APIs in Python, Node.js, React and PostgreSQL, including interfaces for statisticians and programmers to specify, review and run analyses.
• Design and deliver production GenAI and multi-agent systems and RAG pipelines (for example, LangGraph, AutoGen, MCP, A2A), with human-in-the-loop checkpoints, evaluation and observability.
• Deliver on AWS or Azure with CI/CD, monitoring and incident response, and set engineering practices for testing, reliability and security.
• Use AI tools such as GitHub Copilot and Claude Code to speed up prototyping, testing and debugging, and show the team how.
• Build validation-ready systems aligned to GxP and 21 CFR Part 11, with audit trails, qualified environments, independent verification (double programming) and guardrails, confidence scoring and explainability for AI agents, working with Quality and computer system validation (CSV) teams.

What you'll need

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field.
• 10+ years of software engineering experience, including 4+ years leading technical delivery or architecture for production systems.
• Track record of defining architecture direction and technology roadmaps, with production implementations adopted as reference patterns or platform standards.
• 2+ years building production GenAI or LLM solutions, including agentic patterns (tool use, planning, multi-agent orchestration).
• At least one agent framework (for example, LangGraph, AutoGen, CrewAI) and working knowledge of MCP and A2A.
• Strong expertise across the LLM stack and RAG: prompt and context engineering, function calling, structured outputs, embeddings, vector databases (for example, pgvector), hybrid search and rigorous evaluation of agent performance.
• Strong Python, with working knowledge of Node.js (JavaScript/TypeScript), React and PostgreSQL, including schema design and query optimization.
• Experience building MLOps and data pipelines on AWS or Azure with CI/CD, model versioning and monitoring.
• Docker, Kubernetes, microservices and event-driven and distributed systems experience.
• Experience with API and integration design, automated testing and security fundamentals (authentication, authorization, secrets management).
• Working knowledge of R and SAS and of clinical trial statistical programming workflows, including statistical analysis plans, mock shells, SDTM and ADaM datasets, TFL generation, define.xml and CDISC standards.
• Understanding of responsible AI (hallucination mitigation, guardrail design, explainability) and active knowledge of the latest GenAI and agentic AI developments.
• Track record of coaching engineers through code reviews, pairing and architecture spikes and of influencing technical decisions across multiple teams through shipped work.
• Ability to lead through ambiguity, influence senior leaders and non-technical partners and deliver complex programs on time.

Nice to have

• Experience in pharma, life sciences, or other regulated, validated environments (GxP, 21 CFR Part 11, CSV), including validation and audit-readiness practices.
• Familiarity with CDISC standards, clinical trial data flows and regulatory submission processes, including eCTD and analysis data reviewer's guides.
• Familiarity with statistical methods commonly used in clinical trials (for example, survival analysis and mixed models for repeated measures).
• Experience with open-source R ecosystems for pharma (for example, tidyverse and pharmaverse) and with Shiny for interactive review of outputs.
• Experience with agent design patterns (for example, ReAct, plan-and-execute), agent observability and evaluation frameworks.
• Exposure to Databricks or similar data platforms, NoSQL databases and platform engineering.
• Experience with SAFe or product-oriented delivery models, including PI planning.

Details

• Location: Bengaluru, India.
• Employment type: Full-time.
• Individual-contributor technical leadership role with no direct reports.