Senior Machine Learning Engineer

Biopharma Careers; Hyderabad; On-site; INR 2,000,000 - 3,200,000; Full time

Job summary

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

You will build MLOps foundations, containerize models, and establish CI/CD practices to ensure reliable deployment, observability, and reproducibility across scientific workflows.

Qualifications

• Doctorate degree with 4+ years in Data Science, CS, Computational Biology, Bioinformatics, computational chemistry, or related field.
• Or Master’s degree and 8+ years of directly related experience.

Responsibilities

• Design, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into platforms and workflows.
• Package, containerize, and serve models for batch and real-time inference.
• Productionize research models with 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 for code quality and reproducibility across ML applications.
• Monitor model performance, data quality, drift, service health, and issues.

Skills

Python

MLOps

Docker

Kubernetes

APIs

CI/CD

Cloud platforms

PyTorch

TensorFlow

scikit-learn

Education

Doctorate degree

Master's degree

Tools

MLflow

Model registries

Job description

Career Category

Clinical

Job 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.