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SWITS DIGITAL Private Limited
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Machine Learning Engineer

Machine Learning Engineer

SWITS DIGITAL Private Limited

5 years

0 Lacs

gurugram haryana india

Posted:13 hours ago| Platform:

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Skills Required

learning design ml data devops monitoring preprocessing model training evaluation deployment azure docker fastapi flask github jenkins logging scalability reliability security engineering python tensorflow pytorch containerization aws sagemaker gcp vertex ai software testing code kubernetes orchestration spark databricks hadoop agile scrum

Work Mode

On-site

Job Type

Full Time

Job Description

Job Title:

Machine Learning Engineer

Experience Required:

5+ years

Location:

Job Summary

We are seeking a skilled

Machine Learning Engineer

to design, build, deploy, and maintain scalable ML solutions. The ideal candidate will have strong hands-on experience in building

end-to-end ML pipelines

, deploying models into production, and setting up CI/CD workflows. You will collaborate with data scientists, DevOps, and product teams to deliver high-quality ML solutions with robust monitoring and performance tracking.

Key Responsibilities

Build and manage end-to-end ML pipelines (data ingestion → preprocessing → model training → evaluation → deployment → monitoring).
Deploy ML models using Azure ML Endpoints, Docker, FastAPI, or Flask.
Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.
Monitor and log ML models in production using Azure Monitor, Application Insights, or custom logging frameworks.
Ensure scalability, reliability, and security of ML systems in production.
Collaborate with cross-functional teams to integrate ML models into business applications.
Troubleshoot, optimize, and fine-tune ML models and infrastructure.

Required Skills & Qualifications

5+ years of experience in Machine Learning / MLOps / Data Engineering.
Strong knowledge of Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Hands-on experience with end-to-end ML pipelines including data preprocessing, training, and deployment.
Proficiency in containerization and deployment tools (Docker, FastAPI, Flask).
Experience with Azure ML (preferred), AWS Sagemaker, or GCP Vertex AI.
Strong understanding of CI/CD tools (Azure DevOps, GitHub Actions, Jenkins).
Familiarity with monitoring tools (Azure Monitor, Application Insights, Prometheus, Grafana).
Solid understanding of software engineering best practices (version control, testing, code reviews).
Bachelor’s/Master’s degree in Computer Science, Data Science, or related field.

Good To Have

Experience with Kubernetes or container orchestration tools.
Exposure to big data platforms (Spark, Databricks, Hadoop).
Knowledge of feature stores, ML model registries, and model explainability.
Familiarity with Agile/Scrum methodology.

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Machine Learning Engineer