$ cat jobs/tech-lead-manufacturing-data-ai-platforms-global-pharmaceuti-toptal-eced10e10618.json
Tech Lead - Manufacturing Data & AI Platforms - Global Pharmaceutical Company
<p> <strong>Headquarters:</strong> </p> <p><strong>Summary</strong><br>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.</p><p><strong>General information</strong><br>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.</p><p>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.</p><p><strong>Tasks and Deliverables</strong><br>- Own delivery of a portfolio of manufacturing data products from concept through validated production use.<br>- Lead a small, senior, multi-disciplinary team of data engineers, warehouse engineers, and full-stack developers.<br>- Solve technical issues that stall the team, including pipeline failures, warehouse performance and cost, data quality, integration with plant systems, and production incidents.<br>- Supervise progression from DEV to QA to PROD, ensuring validation, change control, traceability, and documentation are properly met.<br>- Partner with data science to frame solvable problems, provide well-understood data and environments, and assess whether models are fit for intended production use.<br>- Evaluate and introduce AI-assiste
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