Senior Machine Learning Engineer

Amgen Technology; Hyderabad; On-site; INR 3,500,000 - 5,200,000; Full time

Job summary

Amgen Technology seeks a Software/ML Engineer to bring predictive models into production for biologics discovery. You will partner with ML scientists, software engineers, and data engineers to transform prototypes into scalable services and establish robust MLOps foundations.

You will design and deploy production-grade ML services, containerize models, and productionize research models with reliability, monitoring, and reproducibility.

Qualifications

• Doctorate or Master's with extensive experience in data science or related field.
• Experience building production ML systems, APIs, and data-driven apps.
• Strong Python programming and software engineering fundamentals.
• Hands-on experience with MLOps tools, CI/CD, and model lifecycle management.

Responsibilities

• Design, build, and deploy production ML services and APIs that integrate models.
• Containerize and serve models for batch and real-time inference.
• Productionize research models with reliability, scalability, and testing.
• Establish MLOps practices for experiment tracking, deployment, and rollback.
• Implement CI/CD pipelines for ML applications to ensure quality and reproducibility.
• Monitor model performance, data quality, drift, and system health.
• Collaborate with ML scientists, data engineers, and software engineers.
• Communicate designs, trade-offs, and metrics to technical and scientific partners.

Skills

Python

ML fundamentals

Communication

Education

Doctorate in Data Science or related field

Master's + 8+ years experience

Tools

MLflow

Docker

Kubernetes

REST/gRPC APIs

AWS

Databricks

Spark

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.