Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Amgen. Hyderabad · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Turn predictive models into production services, APIs and applications for biologics discovery workflows
- Package models for batch and real-time use, then improve their reliability, testing and maintainability
- Set up model tracking, version control, validation, deployment and rollback practices
- Build CI/CD pipelines and track model, data and service health to investigate production issues
- Create repeatable data, training, inference and evaluation workflows with scientists
What you bring
- A doctorate plus at least four years of relevant experience, or a master's degree plus at least eight years
- Strong Python and software engineering skills, including testing, code review, documentation and version control
- Hands-on work with MLOps practices such as experiment tracking, model registries and lifecycle management
Who this fits
This role suits a senior engineer who has helped move machine-learning research into supported production systems. You'll work with scientific and engineering partners on tools and workflows for biologics discovery.
From the employer
Career Category
ClinicalJob Description
What you will do
Let's do this. Let's change the world. Amgen’s AI & Data for Engineered Biologics team within Large Molecule Discovery is seeking a Software/ML Engineer to help bring predictive models and ML-enabled tools into production for biologics discovery.
In this role, you will partner with ML scientists, software engineers, data engineers, and discovery teams to transform research prototypes into scalable, tested, and maintainable services. You will build the MLOps foundations that make models easier to deploy, reproduce, monitor, and integrate into scientific workflows.
Key Responsibilities
Design, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into LMD platforms and scientific workflows
Package, containerize, and serve models for batch and real-time inference
Productionize research models by improving reliability, scalability, testing, and maintainability
Establish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollback
Implement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applications
Monitor model performance, data quality, data/model drift, service health, usage and troubleshoot issues
Build and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientists
Evaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalability
Communicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Software/ML Engineer with these qualifications.
Basic Qualifications
Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field
Or
Master's degree and 8+ years of directly related experience
Preferred Qualifications
Experience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applications
Strong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version control
Hands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle management
Experience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patterns
Familiarity with AWS, Databricks, Spark, or similar cloud/data platforms
Experience with model observability, logging, alerting, drift detection, and production troubleshooting
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inference
Ability to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineers
Strong ownership, problem-solving, and communication skills, with demonstrated contributions to production ML systems, open-source MLOps tools, or publications in venues such as MLSys, NeurIPS, ICML, ICLR, or comparable venues; candidates should highlight representative work on their resume.