The Analytics & GenAI Architect, Life Sciences Technology, designs and governs analytics and AI-enabled decision solutions that support commercial, market access, patient services, and medical affairs stakeholders. This role owns semantic architecture, KPI mart design, decision product development, and enterprise-grade GenAI orchestration patterns. The architect ensures that analytics and AI solutions are scalable, explainable, production-ready, and aligned to defined business logic. This role operates at the intersection of data engineering, applied AI, and business translation — ensuring decision intelligence products are reliable, governed, and built for sustained enterprise use.
7–10 years of experience in analytics engineering, BI architecture, data science with at least 3 years of experience in Gen AI solution delivery. Strong expertise in Python with experience building scalable AI and analytical models. Experience designing multi-agent topologies (supervisor–worker, router, debate) with explicit planning, memory, and tool-use policies. Experience applying structured output and function-calling. Working knowledge of RAG architecture, vector search, embeddings, and reliable orchestration. Experience implementing advanced planning and reasoning patterns (React-style tool reasoning, task decomposition, dynamic routing), with guardrails for prompt injection, data leakage, and tool misuse. Expertise in Agentic AI frameworks leveraging LangChain and LangGraph. Knowledge of MCP servers and tool integration. Experience implementing production-ready practices including testing, monitoring, CI/CD integration, and observability. Experience collaborating across distributed engineering teams. Bachelor’s degree in data science, computer science, engineering, or related field.