We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive advantages. Design and build AI for Engineering: agents, copilots, and LLM-assisted tooling that predict and fix data quality problems and improve the team's delivery speed.

Build the observability layer — monitoring, alerting, dashboards — that surfaces issues before they affect downstream consumers.

Develop ETL/ELT pipelines on Databricks, PySpark, SQL, and AWS (Glue, Lambda, S3, Athena) that hold up at production scale.

Lead root cause analysis on production issues, driving fixes and process changes that prevent recurrence.

Partner closely with the Platform Engineering team so tooling and infrastructure integrate cleanly.

Bring CI/CD and infrastructure-as-code discipline to how the team ships.

Mentor junior engineers on observability, automation, and applied use of AI to improve engineering delivery.

Strong command of Python, PySpark, and SQL — sufficient to build automation and monitoring tooling, not just write transformation logic.

Real production experience running a Databricks environment — Delta Lake internals, Unity Catalog, Workflows.

A track record of building AI agent or LLM tooling that has measurably improved an engineering team's delivery.

Working depth in AWS's data services — Glue, Lambda, S3, Athena — sufficient to inform architecture decisions.

A habit of writing maintainable code, reviewing others' code constructively, and having previously set technical standards for a team.

Sound understanding of data governance, with the ability to build access controls in rather than add them after the fact.

Strong root-cause-analysis discipline and the ability to communicate technical findings clearly to both engineers and non-technical stakeholders.

Qualifications & Experience

- A degree in Computer Science,



Engineering, Data Science, Information Systems, or a related field.
- 6–8 years of experience building and owning production systems, ideally with substantial Databricks experience.
- Experience building monitoring or data-quality tooling from the ground up, rather than configuring existing dashboards.
- Experience building AI-for-Engineering tooling that has measurably improved a team's delivery — this is a key differentiator for this role.
- Preferred: experience with pharma commercial data (claims, sales, payer, patient, HUB) and the integration challenges typical of these datasets.

Preferred: AWS, Databricks, or related certifications.

Where you work matters – because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. Supporting People With Disabilities

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to [HIDDEN TEXT]. Data Protection

We will never request payments, financial information, or social security numbers during our application or recruitment process. Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.

If this posting is missing required information required by local law or incorrect, contact BMS at [HIDDEN TEXT] with the Job Title and Requisition number. To check your application status, please login to your Candidate Home Account.

Data & AI Engineer, Commercialization

📌 Senior AI/Data Science Engineer (Secunderabad)
🏢 Bristol-Myers Squibb
📍 Secunderabad