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 explicit 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.

📌 AI Architect (Bengaluru)
🏢 Axtria
📍 Bengaluru