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Cybage Software
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Machine Learning Engineer

Machine Learning Engineer

Cybage Software

5 years

0 Lacs

Pune Maharashtra India

Posted:3 days ago| Platform:

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Skills Required

learning development deployment ml engineering model data integration leadership design mentoring training support coding regression tensorflow pytorch latency efficiency tuning evaluation metrics analysis strategies mlflow monitoring management devops aws sagemaker gcp vertex ai azure collaboration code python algorithms pipeline airflow spark kafka pyspark containerization docker kubernetes analytics certifications agile tracking

Work Mode

On-site

Job Type

Full Time

Job Description

Job Title:

Job Summary:

Machine Learning Lead Engineer

Roles and Responsibilities:

Technical Leadership & Delivery

Lead the end-to-end development of ML models and pipelines—from data preparation and feature engineering to model training, validation, and deployment.
Translate business requirements into scalable ML solutions, ensuring performance, maintainability, and production readiness.
Supervise and support team members in their project work; ensure adherence to coding and MLOps best practices.

Model Development & Optimization

Design and implement machine learning models (e.g., classification, regression, NLP, recommendation) using TensorFlow, PyTorch, or Scikit-learn.
Optimize models for accuracy, latency, and efficiency through feature selection, hyperparameter tuning, and evaluation metrics.
Conduct model performance analysis and guide the team in continuous improvement strategies.

MLOps & Productionization

Implement robust ML pipelines using MLflow, Kubeflow, or similar tools for CI/CD, monitoring, and lifecycle management.
Work closely with DevOps and platform teams to containerize models and deploy them on cloud platforms like AWS SageMaker, GCP Vertex AI, or Azure ML.
Ensure monitoring, alerting, and retraining strategies are in place for models in production.

Team Collaboration & Mentorship

Guide and mentor a team of ML engineers and junior data scientists.
Collaborate closely with architects, data engineers, and product teams to ensure seamless integration of ML components.
Contribute to code reviews, design sessions, and knowledge-sharing initiatives.

Skills and Qualifications:

Must-Have Skills

5+ years of experience in ML engineering or applied data science, with a strong track record of delivering production-grade ML systems.
Deep expertise in Python, data structures, algorithms, and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with data pipeline tools (Airflow, Spark, Kafka, Pyspark) and MLOps platforms (MLflow, Kubeflow).
Strong knowledge of cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).
Solid understanding of model deployment, monitoring, and lifecycle management.

Good-to-Have Skills

Prior experience leading small to mid-sized technical teams.
Exposure to business intelligence or data analytics.
Cloud certifications (e.g., AWS Certified ML Specialty).
Familiarity with agile methodologies and project tracking tools like Azure DevOps.

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