ML Engineer

Wertone IT Services Private Limited; Nagpur District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Wertone IT Services Private Limited in Nagpur is seeking a Machine Learning Engineer to build, deploy, and maintain ML systems that solve real-world business problems. You will bridge data science and software engineering.

Responsibilities include data preparation, feature engineering, model development and deployment, API/services, monitoring, and retraining for data drift. You will apply Python, SQL, ML frameworks, and MLOps tools to deliver reliable production-ready solutions.

Qualifications

• Data preparation, feature engineering, and model development are required.
• Experience in deploying ML models to production with APIs/services.
• Proficiency in Python, SQL and ML frameworks is expected.

Responsibilities

• Data Preparation: Collect, clean, and preprocess data and create features.
• Model Development & Deployment: Train/evaluate models and deploy into production.
• Monitoring & Retraining: Track performance and retrain for data drift.
• Software Engineering: Write clean, scalable code with CI/CD and version control.

Skills

Python

SQL

Data structures

Algorithms

Statistics

Linear algebra

Machine learning

Deep learning

MLOps

Cloud platforms

Tools

Scikit-learn

TensorFlow

PyTorch

MLflow

Kubeflow

Docker

Kubernetes

Spark

Job description

A Machine Learning (ML) Engineer builds, deploys, and maintains machine learning systems that solve real-world business problems. They bridge the gap between data science and software engineering.

Responsibilities

Data PreparationCollect, clean, and preprocess data.Create features that improve model performance.Model DevelopmentTrain and evaluate machine learning models.Select the best algorithms for the problem (e.g., classification, regression, recommendation).Model DeploymentDeploy trained models into production.Build APIs or services so applications can use the models.Model MonitoringTrack model accuracy and performance over time.Retrain models when data changes (model drift).Software EngineeringWrite clean, scalable code.Use version control (Git), testing, and CI/CD pipelines.Cloud and InfrastructureWork with cloud platforms like AWS, Azure, or GCP.Use containers (Docker), orchestration (Kubernetes), and MLOps tools.Common SkillsProgramming: Python (most important), SQL, sometimes Java or C++Machine Learning: Scikit-learn, TensorFlow, PyTorchData Processing: Pandas, NumPy, SparkDatabases: SQL, NoSQLMLOps: MLflow, Kubeflow, Docker, KubernetesCloud: AWS, Azure, GCPTypical WorkflowUnderstand the business problem.Gather and clean data.Train and evaluate models.Deploy the best model.Monitor and improve the model in production.ML Engineer vs. Data ScientistData Scientist: Focuses on analyzing data, experimenting with models, and generating insights.ML Engineer: Focuses on building reliable, scalable systems that deploy and serve those models in production.

What we're looking for

Python and SQLData structures & algorithmsStatistics and linear algebraMachine learning fundamentalsDeep learningMLOps (Docker, Kubernetes, CI/CD)Cloud platforms (AWS/GCP/Azure)Build end-to-end ML projects and deploy them.