AI Architect

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.