PD Sr Engineer

Job highlights

• Hands-on experience with Databricks,AWS,MLflow,Docker,Kubernetes,GitHub Actions,Azure DevOps,Lakehouse Monitoring,Evidently AI . Strong expertise in Python,PySpark and knowledge of Machine Learning - skLearn,PyTorch .
• Demonstrated experience deploying,operating,monitoring,and maintaining production ML solutions through multiple lifecycle stages
• Required Qualifications

Job Summary

Pharmaceutical Product Development (PD) is increasingly leveraging scalable, reusable, and trusted models for the development of drug substances and drug products. We are seeking a technically strong and experienced Senior Engineer who will drive model lifecycle sustainability efforts to ensure that the performance of machine learning, statistical, and other data-driven models within PD is reproducible, observable, governed, and valuable long after initial deployment. Such models include, but are not limited to, manufacturing process models, product performance models, analytical method and stability models, and material attribute simulation.

In this role, you will help shape implement how such models are assessed, deployed, monitored, maintained, enhanced, and retired within PD s Model Hub, and build the reusable MLOps capabilities, workflows, and standards that will enable Product Development teams scale models with confidence.

This role requires deep understanding of MLOps, machine learning, platform thinking, stakeholder engagement, and scientific collaboration with PD functions such as biologics development, chemical process development, drug product development, and analytical development.

What You Will Do

• MLOps: Contribute to the development and implementation of MLOps frameworks, standards and best practices in collaboration with IT and Data Scientists, reducing the time from model prototype to production deployment

• Model onboarding and operationalization: Deployment and operationalization of advanced machine learning, statistical, and data-driven models within PD s Model Hub

• End-to-end model lifecycle workflow: Develop and implement robust model lifecycle workflows including validation, deployment, monitoring, retraining, versioning, and continuous improvement.

• Model sustainability standards: Design scalable approaches for model discoverability, reproducibility, and governance.

• Model monitoring, Data Drift Model Drift: Build a fit-for-purpose model observability strategy monitoring Model performance, Model Data Drift, Infrastructure health, further developing alerting mechanisms

• Model discoverability and reuse: leveraging PD s Model Hub through lineage, and metadata capture mechanisms that work across programs and modalities.

  What Makes You Successful

• You work effectively in ambiguous environments, proactively identifying challenges opportunities and independently develop solutions while engaging stakeholders as needed.

• You identify the right problem and operating constraint before selecting a technology

• You challenge assumptions and distinguish a compelling prototype from a sustainable enterprise capability.

• You deconstruct complex scientific questions into testable, governable, and reusable components.

• You balance scientific rigor, engineering quality, user experience, speed, risk, and practical business outcomes.

• You communicate clearly, influence without authority, and create alignment across scientists, engineers, product teams, and governance partners.

  Measures of Impact

• Reduced cycle time and rework in onboarding models from development into operation.

• Improved reproducibility, observability, reliability, and reuse of onboarded models.

• Earlier detection and effective resolution of data quality, data drift, model drift, and operational performance issues.

• Greater adoption of standardized lifecycle, MLOps, validation, monitoring, and documentation practices.

• Clearer ownership and lower sustainability risk across PD s model portfolio.

  Required Qualifications

• Degree in Computer Science, Engineering, Statistics, Data Science or related discipline.

• At least 4 years of industry experience working with MLOps practices including Git, CI/CD, automated testing, model registries, experiment tracking, observability, versioning, and governance.

• Hands-on experience with Databricks, AWS, MLflow, Docker, Kubernetes, GitHub Actions, Azure DevOps, Lakehouse Monitoring, Evidently AI

• Strong expertise in Python, PySpark and knowledge of Machine Learning - skLearn, PyTorch

• Practical hands-on expertise in data drift and model drift analysis, including baseline design, metric selection, thresholding, root-cause analysis, and remediation decisions

• Demonstrated experience deploying, operating, monitoring, and maintaining production ML solutions through multiple lifecycle stages.

• Ability to translate complex scientific and technical needs into scalable platform capabilities, standards, and adoption roadmaps.

• Strong communication and stakeholder management skills.

  Preferred Qualifications

• Knowledge of model risk management, validation, change control, data integrity, privacy, security, and responsible AI principles.

• Exposure to GenAI, AI agents, LLM-powered workflows, evaluation frameworks, and human-in-the-loop controls.

• Experience influencing technical standards and communities of practice across distributed, cross-functional teams.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.