ML ENGINEER II
At a glance
Mid-level Machine Learning Engineering role at TE Connectivity. Bangalore.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Prepare structured and unstructured engineering data, checking its quality and building scripts to transform it
- Explore datasets for patterns, relationships, and anomalies that could affect model work
- Build regression, classification, clustering, and anomaly-detection models, then assess their results with suitable metrics
- Help put models into production tools, APIs, and data flows alongside software engineers
- Track live model quality and support retraining when incoming data shifts
What you bring
- A relevant technical degree plus experience: four years with a bachelor’s, three with a master’s, or one with a PhD
- Programming experience in Python or R, with hands-on use of a framework such as PyTorch or TensorFlow
- Experience with pandas or NumPy, data pipelines, model evaluation, and ML experimentation
- Working knowledge of version control and software testing
Who this fits
This role suits an engineer with a software and analytical foundation who wants to apply machine learning to engineering problems and collaborate across engineering teams. Occasional travel for team meetings or training may reach ten percent.
From the employer
Job Overview
The pace of innovation in artificial intelligence and machine learning continues to accelerate, and the Data and Devices Business Unit is at the center of this transformation. Our customers are building intelligent systems that redefine how data is processed, analyzed, and used across compute, networking, and cloud infrastructure. At TE Connectivity, we support this evolution by delivering scalable, high-quality software solutions that enable next-generation products.
This role is a strong fit for someone with a solid software and analytical foundation who is eager to apply machine learning techniques to real engineering problems. You will work closely with experienced engineers, data scientists, and cross functional teams to develop models, improve workflows, and support the integration of ML capabilities into our tools and processes.
Job Responsibilities
Data Collection and Preprocessing
Collect and process structured and unstructured datasets from engineering systems, databases, and operational tools.
Clean and validate datasets to ensure accuracy and consistency.
Develop scripts and pipelines for data preprocessing and transformation.
Exploratory Data Analysis
Perform exploratory analysis to identify patterns, correlations, and insights within datasets.
Investigate data quality issues and anomalies that may impact model development.
Machine Learning Model Development
Implement machine learning algorithms such as regression, classification, clustering, and anomaly detection.
Support feature engineering and model training workflows.
Evaluate model performance using statistical and machine learning metrics.
Model Deployment and Integration
Assist in deploying machine learning models into production systems.
Support integration of models into engineering tools, APIs, and data pipelines.
Work with software engineers to ensure models operate reliably in production environments.
Performance Monitoring
Monitor deployed models for accuracy and performance degradation.
Support retraining and model updates when data drift occurs.
Required Skills & Competency
Bachelor’s with 4+ years, Master’s with 3 years, or PhD with 1 years of experience in Computer Science, Data Science, Software Engineering, Electrical Engineering, or a related technical discipline.
Programming experience in R or Python
Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit learn
Experience working with data analysis libraries such as pandas or NumPy
Familiarity with data pipelines, model evaluation, and machine learning experimentation
Knowledge of software development practices, including version control and testing
Strong analytical thinking and problem-solving skills
Ability to collaborate effectively in cross-functional engineering teams
Clear communicator who works well in a collaborative team environment.
Self-motivated and eager to learn new technologies with guidance.
Ability to travel occasionally, up to 10 percent, for team meetings or training as needed.