Lead Machine learning engineer

Elanco

3 - 8 years

10 - 14 Lacs

bengaluru

Posted:5 days ago| Platform:

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Skills Required

computer science automation prototype machine learning data structures data quality scm open source monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Custom Model Development: Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.
Third-Party Model Utilization: Identify, tune and deploy third-party ML models, covering proprietary and open-source models.
Production Deployment: Manage the deployment of ML models into our production environments, ensuring they are scalable, reliable, and performant.
MLOps and Automation: Build and maintain robust MLOps pipelines for Continuous Integration/Continuous Delivery (CI/CD), model monitoring, and automated retraining.
Data Pipeline Construction: Collaborate with data engineers/stewards to build and optimize data pipelines that feed ML models, ensuring data quality and efficient processing for both training and inference.
Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.
Code and System Quality: Write clean, maintainable, and well-tested production-grade code. Uphold high software engineering standards across all projects.
Performance Tuning: Monitor and analyze model performance in production, identifying opportunities for optimization and iteration.

What You Need to Succeed (minimum qualifications):

Educational Background: A Bachelor s or master s degree in computer science, Software Engineering, Artificial Intelligence, or a related quantitative field.
Programming Excellence: Advanced proficiency in Python and deep experience with core ML/data science libraries (e. g. , PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
Software Engineering Fundamentals: Strong foundation in software engineering principles, including data structures, algorithms, testing, and version control (Git).

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