Tech Lead - Manufacturing Data & AI Platforms - Global Pharmaceutical Company

Embedded Shishya; Gopalganj; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Embedded Shishya in India seeks a Tech Lead to own delivery of manufacturing data products in a regulated setting, shipping in validated production environments and applying AI with sound judgment.

You will lead a senior team of data engineers, warehouse engineers, and full-stack developers, ensuring robust DEV-to-PROD promotions with proper validation and documentation.

Qualifications

• Degree in engineering, computer science, or a related technical discipline, or equivalent demonstrated capability.
• 3+ years leading technical delivery for a software or data team.
• Advanced SQL with strong data modeling judgment and cost awareness.
• Strong Python for production data engineering and services.
• Hands-on AWS experience across compute, storage, orchestration, identity, and observability.
• Deep experience with a cloud data warehouse; Snowflake preferred.
• Fluency with Git-based workflows, CI/CD, infrastructure as code, and release management.
• Working understanding of the machine learning lifecycle, including model evaluation, deployment, and monitoring.
• Strong stakeholder communication skills and the judgment to challenge requests with better technical paths.
• Good understanding of agentic AI.

Responsibilities

• Own delivery of a portfolio of manufacturing data products from concept through validated production use.
• Lead a small, senior, multi-disciplinary team of data engineers, warehouse engineers, and full-stack developers.
• Solve technical issues stalling the team, including pipeline failures and performance/cost concerns.
• Supervise progression from DEV to QA to PROD with proper validation and traceability.
• Collaborate with data science to define problems and ensure data readiness for production models.
• Assess AI-assisted approaches with judgment on regulated-context use.
• Coordinate with Product Owners and Architects to shape scope and sequencing.
• Produce architecture docs, decision records, and runbooks for long-term delivery.

Skills

Advanced SQL

Python (Production)

Git-based workflows

CI/CD

Infrastructure as Code

Data modeling

Machine learning lifecycle

AWS

Communication

Regulated environment awareness

Education

Engineering, CS or related technical discipline

Tools

Snowflake

OSIsoft PI / AVEVA

LIMS

Cloud platform tooling

Job description

Headquarters:

Summary

We are looking for a Tech Lead to take ownership of a delivery portfolio within a Manufacturing Engineering group that builds the data products the plant depends on. This is a hands‑on leadership role focused on shipping and operating manufacturing data products in a validated production environment, while applying AI with sound judgment in a regulated context.

General information

This group builds manufacturing data products including real‑time batch progression monitoring, process anomaly detection, and analytics that drive yield and throughput improvement. The team owns the data pipelines, cloud platform, environments, and delivery, and works closely with data science colleagues to define problems, provide data, and run the infrastructure required for production use.

The role leads a small, senior, multi-disciplinary team and carries accountability for what is shipped, when it is shipped, and how it performs in operation. The environment requires disciplined promotion from DEV to QA to PROD, with validation, change control, traceability, and documentation handled rigorously.

Tasks and Deliverables

• Own delivery of a portfolio of manufacturing data products from concept through validated production use.
• Lead a small, senior, multi-disciplinary team of data engineers, warehouse engineers, and full-stack developers.
• Solve technical issues that stall the team, including pipeline failures, warehouse performance and cost, data quality, integration with plant systems, and production incidents.
• Supervise progression from DEV to QA to PROD, ensuring validation, change control, traceability, and documentation are properly met.
• Partner with data science to frame solvable problems, provide well‑understood data and environments, and assess whether models are fit for intended production use.
• Evaluate and introduce AI-assisted approaches to development, testing, documentation, and monitoring, with clear judgment on regulated‑context limitations.
• Work closely with Product Owners and Product Architects to shape scope and sequencing, and communicate progress, risk, and trade‑offs clearly.
• Produce architecture documentation, decision records, and operational runbooks that support long‑term delivery and operations.

Required experience

• Degree in engineering, computer science, or a related technical discipline, or equivalent demonstrated capability.
• Three or more years leading technical delivery for a software or data team.
• Advanced SQL with strong data modeling judgment and performance and cost awareness.
• Strong Python for production data engineering and services.
• Hands‑on AWS experience across compute, storage, orchestration, identity, and observability.
• Deep experience with a cloud data warehouse; Snowflake preferred.
• Fluency with Git‑based workflows, CI/CD, infrastructure as code, and structured environment promotion and release management.
• Working understanding of the machine learning lifecycle, including model evaluation, deployment, and monitoring.
• Strong stakeholder communication skills and the judgment to challenge requests with better technical paths.
• Good understanding of agentic AI.

Nice to have

• Experience in pharmaceutical, biotech, medical device, or another regulated manufacturing environment.
• Familiarity with GxP computerized system validation, GAMP 5, 21 CFR Part 11, EU Annex 11, or ALCOA+ data integrity principles.
• Exposure to MES, process historians such as OSIsoft PI or AVEVA, ISA‑88 batch structures, electronic batch records, or LIMS.
• Postgraduate qualification in artificial intelligence, machine learning, or data science.
• Practical experience with AI‑assisted development tooling in an enterprise setting.
• Background in cloud security, platform engineering, or technology evaluation and validation.