Senior Machine Learning Engineer

Amgen; Hyderabad; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Amgen’s AI & Data for Engineered Biologics team is hiring a Software/ML Engineer to productionize predictive models and ML-enabled tools for biologics discovery. You will partner with ML scientists, software/data engineers to transform prototypes into scalable services.

You will establish MLOps, containerize models, and implement CI/CD pipelines for reliable inference and maintainability across scientific workflows.

Qualifications

• Doctorate or Master’s with related experience in data science, ML, or computational biology.
• Strong programming and software engineering fundamentals.
• Experience building production ML systems and analytics apps.

Responsibilities

• Design, build, and deploy production-grade ML services and APIs integrated into discovery platforms.

Skills

Python programming

Software engineering

MLOps

CI/CD

Docker

Kubernetes

REST/gRPC APIs

Cloud platforms

Communication

collaboration with scientists

Education

Doctorate or equivalent in Data Science/CS

Master's degree with 8+ years experience

Tools

MLflow

Model registries

Spark

Databricks

TensorFlow/PyTorch

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+ years 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.