Engineer - ML
Eaton (company site, opens in a new tab)3–5 yrsPuneHybridDirect career pageEst. 12–20 LPA
Posted 1d ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Eaton
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Eaton you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, deploy, and maintain scalable machine learning platforms and MLOps solutions in cloud environments.
- Build and optimize data engineering pipelines, feature engineering workflows, and CI/CD pipelines for ML/AI applications.
- Troubleshoot and improve model performance, data quality, pipeline reliability, and ensure high availability and scalability of production ML systems.
Minimum Requirements
- Bachelor's degree in Computer Science or equivalent software engineering discipline.
- 3–5 years of professional experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
- Hands-on experience with big data technologies (Apache Spark, Kafka, Hadoop, Flink) and large-scale data pipelines (Python, Spark, Databricks, Airflow).
- Experience with CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins), cloud infrastructure (AWS, Azure, or GCP), and containerization (Docker, Kubernetes).
Ideal Candidate Profile
- Experienced in designing and managing end-to-end machine learning production systems with strong focus on automation and reliability.
- Skilled in both ML algorithms (regression, classification, deep learning) and software engineering best practices (OOP, design patterns, testing, code reviews).
- Comfortable working in cross-functional teams and contributing to architecture and technical design discussions involving cloud, MLOps, and data engineering technologies.