AI Architect

Job description

Role Summary We are looking for an AI Architect to lead the design and delivery of AI-first commercial data platforms for life sciences clients. The solutions range from AI powered self-serve front end application development, agentic AI orchestration layers and scalable back-end data engineering/warehousing on cloud-based platforms (Databricks preferred; databricks, snowflake Snowflake), hosted on AWS/Azure stacks. You will serve as the primary technical authority on client engagements defining architecture, governing AI usage, and mentoring delivery teams from pre-sales through production cutover. Key Responsibilities Solution Designing • Define the AI strategy and roadmap with a clear phased delivery plan from MVP to production • Architect and design solutions from an AI first standpoint • Lead RFP responses and proactively shape the AI thought process for clients, including solution scoping, effort estimation, and risk identification. • AI governance : define and enforce Claude boundaries, HITL design, PII protection, guardrails, audit logs • Partner with global product owners, business stakeholders, and platform leads across R&D, HEOR, Commercial, Supply Chain. Technical Delivery • Solution Delivery : • Lead end-to-end delivery using Claude (preferred) as the agentic code generation and orchestration backbone, with well-defined human-in-the-loop checkpoints. • Build ingestion pipelines across structured (clinical, commercial, manufacturing) and unstructured data (medical notes, literature, NLP outputs). • Ensure high-performance ELT/ETL, orchestration, CI/CD, and MLOps practices. • Implement robust data quality KPIs, reconciliation frameworks • Agentic ops : for managing Lakehouse / Warehouses (Databricks, Snowflake, Dataiku). • Team leadership : Lead and mentor small to mid-size teams of data engineers, AI engineers, MDM specialists, and QC leads. • Scope and risk management : proactively identify delivery risks, define mitigations, and communicate status to client and internal stakeholders Domain Expectations • Knowledge of pharma data: • Commercial data (IMS/IQVIA, specialty pharmacy feeds) • Clinical trial data (CDISC, SDTM, ADaM) • RWE/claims/EHR, patient support program data • Manufacturing & QC datasets • Experience delivering data foundations for regulatory submissions, pharmacovigilance, digital biomarkers, omnichannel analytics. Required Experience • 10–15 years in Data Engineering, with 5+ years in pharma/biotech • Proven track record on presales, solution design and solution defence • Ability to present architecture decisions and risk trade-offs to senior client leadership • Hands-on experience on Claude Code • Hands -on experience on Generative AI pipelines, vector databases, feature stores • Hands-on experience on Databricks, Snowflake, Azure/AWS Technical Skillset Core • Claude Code – prompt engineering, agentic pattern design, Git-controlled template versioning • AI/LLM : Experience integrating large language models (Claude, GPT, or equivalent) into data engineering workflows; agentic AI patterns (LangGraph, LangChain, or equivalent) • Snowflake / Databricks • Python, Pyspark, SQL • Tools: Soda, Collibra, Unity, DQ, Airflow, Streamlit, Jenkins • ServiceNow integration for ops support workflows (preferred) Education • Bachelor’s/Master’s in Engineering, Computer Science, Data Science, or equivalent. • Certifications in cloud/data platforms preferred.