14 Aug
|
AMGEN
|
Hyderabad
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 cooperative qualified we seek is a Software/ML Engineer with these
qualifications.
Basic Qualifi
📌 Senior Machine Learning Engineer Hyderabad
🏢 AMGEN
📍 Hyderabad