Machine Learning Engineer (MLOps & AI Infrastructure)

Bei Roche kannst du ganz du selbst sein und wirst für deine einzigartigen Qualitäten geschätzt. Unsere Kultur fördert persönlichen Ausdruck, offenen Dialog und echte Verbindungen. Hier wirst du für das, was du bist, wertgeschätzt, akzeptiert und respektiert. Dies schafft ein Umfeld, in dem du sowohl persönlich als auch beruflich wachsen kannst. Gemeinsam wollen wir Krankheiten vorbeugen, stoppen und heilen und sicherstellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und in Zukunft. Werde Teil von Roche, wo jede Stimme zählt.

Die Position

Machine Learning Engineer (MLOps& AI Infrastructure)

Roche India – Roche Services& Solutions

Hyderabad / Chennai

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love.

That’s what makes us Roche.

Roche has established the Global Analytics and Technology Center of Excellence (GATE) to drive analytics- and technology-driven solutions by partnering with Roche affiliates across the globe. GATE enables data-led decision-making and innovation across healthcare and biotech operations. To learn more about us: visit

As aMachine Learning Engineer (MLOps), you will play a critical role in designing, building, and maintaining scalable machine learning systems within Roche’s data ecosystem. You will collaborate closely with data scientists, data engineers, and business stakeholders to develop production-grade ML infrastructure that supports real-world healthcare and commercial applications. This position demands a blend of technical expertise, problem-solving ability, and strong ownership of MLOps processes to ensure that Roche’s ML models are production-ready, monitored, and continuously improving.

Your Opportunity:

ML Infrastructure and Pipeline Development (Primary Focus):

• Design, build, and maintain scalable production-grade ML pipelines for data ingestion, model training, and inference
• Implement automated workflows for data preprocessing, feature engineering, and model retraining
• Collaborate with data scientists to operationalize ML models and ensure smooth transition from experimentation to production
• Develop reusable frameworks and internal tools to standardize and accelerate ML development lifecycles

Model Deployment and Monitoring (Primary Focus):

• Deploy and manage ML models in production environments using cloud-based services (AWS preferred)
• Implement monitoring frameworks for data drift, model drift, and performance degradation
• Maintain high availability, reliability, and scalability of deployed models through robust engineering practices
• Develop alerting systems to ensure timely remediation and maintenance of production ML systems

Collaboration and Project Ownership (Primary Focus):

• Partner with Stakeholders, data scientists, product managers, and IT teams to translate business requirements into scalable ML architectures
• Take end-to-end ownership of MLOps initiatives, from design through deployment and continuous monitoring
• Champion engineering excellence by enforcing best practices in CI/CD, version control, and automated testing
• Contribute to Roche’s broader AI/ML roadmap by developing infrastructure that supports both traditional ML and emerging GenAI applications

Communication, Mentorship, and Governance (Primary Focus):

• Translate complex data insights into clear and actionable business strategies that address stakeholder needs and expectations
• Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables
• Ensure adherence to Roche’s ethical AI standards and data privacy regulations

GenAI, Automation, and Emerging Technologies (Secondary / Emerging Focus):

• Collaborate with AI research teams to integrate Generative AI solutions into ML workflows and pipelines
• Experiment with LLMs and prompt-based workflows to enhance automation and model explainability
• Support the adoption of workflow orchestration tools such as Kubeflow, Airflow, or MLFlow for model lifecycle management

Who you are:

• You are someone with bachelor’s or master’s degree in Computer Science, Data Science, Machine Learning, or related fields and 4+ years of professional experience in Machine Learning Engineering, Data Engineering, or MLOps roles
• Certifications in MLOps, AWS Cloud, or Data Engineering are highly desirable
• Proven experience building and deploying ML systems at scale in production, with strong understanding of supervised, unsupervised, and NLP models
• Hands-on experience with large-scale data processing using distributed computing frameworks
• Strong analytical, problem-solving, and debugging skills with attention to scalability and reliability
• Demonstrated ability to work independently and take ownership of end-to-end ML systems
• Proficiency in Python, PySpark, and SQL for data engineering and ML workflows
• Experience with scikit-learn, Spark MLlib, TensorFlow, PyTorch, and MLflow
• Extensive hands-on experience with AWS services such as S3, SageMaker, Glue, Lambda, Athena, EMR, and SageMaker Pipelines. Familiarity with GCP or Azure ML environments is a plus
• Expertise in version control (Git/GitHub), CI/CD (GitHub Actions, Jenkins), and model registry workflows
• Experience with Docker and Kubernetes for containerization and orchestration
• Proven track record of building and releasing ML frameworks or internal tools to accelerate model deployment
• Basic understanding of pharmaceutical datasets (e.g., IQVIA, SHA, Patients data) and familiarity with US healthcare markets would be a plus
• Strong analytical and problem-solving skills with a data-driven mindset

Good to Have:

• Experience with Kubeflow, Airflow, or Prefect for ML pipeline orchestration
• Exposure to Generative AI (LLMs, transformers) and integration of GenAI into enterprise ML workflows
• Familiarity with data governance, security, and ethical AI practices in production environments

Note: This job description is intended as a general guideline for the responsibilities and qualifications required for this position. It is not an exhaustive list, and responsibilities may evolve and change based on business needs

Wer wir sind

Eine gesündere Zukunft treibt uns zur Innovation an. Mehr als 100.000 Mitarbeiter weltweit arbeiten gemeinsam daran, wissenschaftliche Fortschritte zu erzielen und sicherzustellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und für zukünftige Generationen. Durch unser Engagement werden über 26 Millionen Menschen mit unseren Medikamenten behandelt und mehr als 30 Milliarden Tests mit unseren Diagnostik-Produkten durchgeführt. Wir ermutigen uns gegenseitig, neue Möglichkeiten zu erkunden, Kreativität zu fördern und hohe Ziele zu setzen, um lebensverändernde Gesundheitslösungen zu liefern.

Gemeinsam können wir eine gesündere Zukunft gestalten.

Roche ist ein Arbeitgeber, der die Chancengleichheit fördert.