About the role
We are looking for strong experience in AI and Data Engineering, preferably with hands-on experience in Generative AI (GenAI). Experience in any cloud platform (AWS, Azure, or GCP) will be considered.
Responsibilities:
- Set technical direction across multiple platforms and client engagements - architecture, standards, and the patterns the team reuses.
- Make the org-level calls: build vs. buy, primitives vs. framework, what becomes a shared internal capability, and what we refuse to put in production without explicit client sign-off.
- Stay hands-on: write the hard parts, prototype the risky ideas, and prove out approaches with real code and real measurements before the team commits.
- Lead empirical technical investigations (failure-mode studies, reproducibility, cost/quality tradeoffs) and turn them into defensible, client-facing points of view.
- Own cross-cutting concerns: cloud architecture on AWS and Azure, data platform architecture on Azure Databricks (medallion architecture, Unity Catalog governance, pipeline scalability, Terraform-based infrastructure provisioning, MLflow-based model lifecycle management), data architecture across warehouse streaming/operational stores, evaluation and observability standards, and security and reproducibility in regulated contexts.
- Multiply the team: set the engineering bar, grow Senior SDEs and SDEs, and represent technical strategy to senior stakeholders and clients.
Requirements:
- Mastery of Python and systems design, with a track record of architecting production systems that others build on.
- Broad and deep data + cloud architecture across AWS and Azure: multi-store data design, distributed systems, streaming, security, and cost.
- Driving Innovation, Empowering Insights.
- Hands-on Azure Databricks architecture experience in production: Unity Catalogue, Delta Lake, cluster policy design, job orchestration, and Terraform provisioning.
- Supply chain and inventory operations domain depth: understanding of demand planning processes, MRP logic, inventory parameters and how planning decisions translate into data requirements - sufficient to engage credibly with supply chain stakeholders without hand-holding.
- Strong opinions, loosely held, grounded in first principles - can derive the trade-off rather than cite the trend, with deep scepticism of unnecessary abstraction.
- Proven ability to build LLM/agentic systems from primitives and to evaluate them rigorously (reproducibility, calibration, cost).
- Influence without authority: sets standards, writes the specs and docs others follow, and elevates the whole team.
- Experience translating technical work into client-facing strategy; pharma/regulated experience strongly valued.