FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Machine Learning Engineer – MLOps, AI Infrastructure
RocheMachine Learning Engineer designing and maintaining scalable machine learning systems within Roche’s data ecosystem. Collaborating with data scientists and engineers to develop production-grade ML infrastructure in healthcare.
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformJenkinsKubernetesPySparkPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
Key responsibilities & impact- 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
- 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
- Take end-to-end ownership of MLOps initiatives, from design through deployment and continuous monitoring
- Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables
- Collaborate with AI research teams to integrate Generative AI solutions into ML workflows and pipelines
Requirements
What you’ll need- Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or related fields
- 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
Benefits
Comp & perks- Professional development opportunities
- Flexible working arrangements
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine Learning EngineeringData EngineeringMLOpsPythonPySparkSQLscikit-learnSpark MLlibTensorFlowPyTorch
Soft Skills
analytical skillsproblem-solvingdebuggingattention to detailindependenceownershipcollaborationproject managementcommunicationbest practices promotion
Certifications
MLOps certificationAWS Cloud certificationData Engineering certification