MLops Engineer

MLops engineer

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About the Role

Machine Learning & MLOps Engineer

designing, deploying, and managing ML solutions

Key Responsibilities

• Design, develop, and optimize

  ML models

  for business-critical use cases.
• Build and manage

  end-to-end MLOps pipelines

  (deployment, monitoring, retraining, lifecycle management).
• Collaborate with data engineers, developers, and cross-functional teams to deliver seamless ML integration.
• Automate workflows for

  data preprocessing, feature engineering, and retraining

  .
• Ensure

  reproducibility, scalability, and observability

  of deployed ML systems.
• Implement

  CI/CD workflows

  , containerization (Docker), and orchestration (Kubernetes).
• Monitor deployed models for performance, drift, and reliability.

Must have skills

• 5+ years of experience in

  ML Engineering & MLOps

  .
• Proficiency in

  Python

  and ML frameworks (

  TensorFlow, PyTorch, Scikit-learn

  ).
• Strong hands-on experience with

  MLOps tools

  (MLflow, Kubeflow, SageMaker, Vertex AI).
• Experience with

  Docker, Kubernetes, CI/CD pipelines

  .
• Strong expertise in

  AWS, Azure, or GCP

  cloud platforms.
• Experience deploying

  scalable and secure ML systems

  .

Good to have

• Experience with

  Big Data tools

  (Spark, Databricks, Hadoop).
• Knowledge of

  Feature Stores

  and real-time pipelines.
• Experience with

  real-time model serving

  and streaming architectures.
• Exposure to

  healthcare or regulated industries

  .