Machine Learning Operations Engineer
20 hours ago
Lilly is a global healthcare leader dedicated to making life better for people around the world. Our 39,000 employees worldwide work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease.
Job OverviewWe are seeking an experienced Machine Learning Operations Engineer to join our Business Insights & Analytics team in India. As a key member of the ML Ops pillar, you will be responsible for managing projects requiring ML Ops expertise across the commercial analytics continuum, helping drive customer experience and business impact.
Responsibilities- Create and automate ML Ops pipelines on cloud and premise, orchestrate CI/CD pipelines across multiple projects
- Refactor, train, retrain, deploy, test, and continuously monitor ML models for drift
- Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
- Optimize model hyperparameters
- Evaluate and explain model performance
- Onboard, operate, and decommission models according to established workflows
- Design and maintain scalable MLOps frameworks to support client-specific models
- Provide technical design solutions to support Proof-of-Concepts (PoVs)
- Measure and improve services by creating benchmarks, metrics, and monitoring
- Implement best practices and run proof-of-concepts for automated and efficient model operations on a large scale
- Coordinate with diverse stakeholders, including statisticians, software engineers, and infrastructure teams, to design optimal ML pipelines
- 4-8 years of demonstrated expertise in building ML/automation pipelines from scratch (including model versioning, data lineage, monitoring, deployment, optimization, scalability, orchestration, continuous learning, Automated pipelines)
- Proficiency in AWS components, such as SageMaker, Lambda, and other AWS services, serverless services, etc.
- Strong knowledge of Python and PySpark, with working knowledge of R preferred
- Experience with popular MLOps frameworks, like Kubeflow, MLFlow, and DataRobot
- Knowledge of frameworks such as scikit-learn, Keras, PyTorch, TensorFlow, etc., and ability to understand tools used by data scientists
- Hands-on experience with Docker, Kubernetes, Jenkins, and EC2, EMR, RDS, Redshift, S3, Athena, Airflow, and similar tools
- Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to data scientists, engineering teams, and business audiences
Bachelor's or Master's degree in Computer Applications/Computer Science or Specialization/courses in ML/DS with deep understanding of SDLC
Salary InformationThe estimated salary range for this position is ₹15,00,000 - ₹25,00,000 per annum, depending on experience and qualifications.
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