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# Machine learning Engineer

- **Company:** Tata Consultancy Services Limited
- **Location:** Pennington, NJ, United States
- **Salary:** $105,000.0 - $125,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Cloud Computing, Encodings, Computer Programming, Continuous Integration, Data Cleansing, Python (Programming Language), Logistic Regression, Machine Learning, NumPy, Tensorflow, Azure Machine Learning, Feature Engineering, Data Ingestion, Pytorch, Random Forest, Apache Spark, Deep Learning, Pandas, Scikit Learn, Kubernetes, Information Technology, Xgboost, Machine Learning Operations, Software Version Control, Data Pipelines, Docker, Databricks
- **Published:** June 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e556b519575fbce1

## About the Role

Do you have experience in Time series models?, Do you have a Bachelor's degree?, Qualifications : BACHELOR OF COMPUTER SCIENCE

## Description

CI/CD for ML, MLflow/Kubeflow/SageMaker/Azure ML
Model versioning, monitoring, retraining workflows
Docker, Kubernetes for deployment
Programming & Cloud:
Strong Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
Experience on AWS/Azure/GCP ML services
Roles & Responsibilities
Looking for a strong hands-on ML Engineer with deep experience in data science, model development, and building scalable data/ML pipelines. Candidate must be technically solid, execution-focused, and able to deliver production-ready ML solutions.
Core Technical Skills Needed
ML Model Development:
Supervised models: Logistic Regression, Random Forest, XGBoost/LightGBM, SVMs
Deep learning: Neural Networks, CNN/RNN, Transformers (PyTorch or TensorFlow) Classification models and evaluation techniques (AUC, ROC, precision/recall, cross-validation)
Data Preparation & Feature Engineering: Data cleaning, handling missing values & outliers Feature scaling, encoding, time-series feature generation Strong EDA and statistical analysis skills
Data Pipeline Engineering: Build and maintain ML/data pipelines using Spark, Databricks, Airflow Data ingestion, transformation, validation (Great Expectations preferred)
MLOps & Deployment: CI/CD for ML, MLflow/Kubeflow/SageMaker/Azure ML
Model versioning, monitoring, retraining workflows
Docker, Kubernetes for deployment
Programming & Cloud: Strong Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
Experience on AWS/Azure/GCP ML services


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