Machine Learning Engineer (MLOps & AI Infrastructure)

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

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 a Machine 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

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.