Senior Machine Learning Engineer

Amgen Inc; Hyderabad; On-site; INR 2,800,000 - 5,500,000; Full time

Job summary

Amgen Inc in Hyderabad is seeking a Software/ML Engineer to bring predictive models into production for biologics discovery. You will partner with ML scientists, software engineers, data engineers, and discovery teams to transform research prototypes into scalable, tested services.

You will build MLOps foundations, establish CI/CD, monitor model performance, and ensure reproducibility across ML applications.

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.
• Experience building 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.

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.

Skills

Python programming

Software engineering fundamentals

Communication skills

Ownership

Collaboration with scientists

Education

Doctorate degree in Data Science/CS/related field

Master’s degree with 8+ years of related experience

Tools

MLflow

Model registries

CI/CD

Docker

Kubernetes

REST/gRPC APIs

AWS

Databricks

Spark

PyTorch

TensorFlow

scikit-learn

Job description

What you will do

Let’s do this. Let’s change the world. Amgens 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.