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Machine Learning Ops Engineer

Machine Learning Ops Engineer

ZinnovBangalore
30+ days ago
Job description

Job Title : Machine Learning Ops Engineer

Job Level : Mid-Level

Job Location : Bangalore, India

Key Responsibilities :

  • Design, implement and maintain ML pipelines for model training, validation, and deployment
  • Automate model deployment processes using CI / CD pipelines and containerization technologies
  • Monitor model performance, data drift, and system health in production environments
  • Collaborate with data scientists to operationalize machine learning models and algorithms
  • Implement version control for models, datasets, and ML experiments using MLOps tools
  • Optimize ML infrastructure for scalability, reliability, and cost-effectiveness
  • Troubleshoot and resolve issues related to model deployment and production systems
  • Maintain documentation for ML workflows, deployment processes, and system architecture
  • This position may require availability outside of standard business hours as part of a rotational on-call schedule.

What You'll Need to Be Successful (Required Skills) :

  • 2-4 years of experience in software development, DevOps, or data engineering
  • Proficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, Scikit-learn
  • Experience with containerization (Docker) and orchestration tools (Kubernetes)
  • Knowledge of cloud platforms such as AWS, Azure, GCP and their ML services
  • Understanding of CI / CD pipelines, version control (Git), and infrastructure as code
  • Familiarity with monitoring tools and logging frameworks for production systems
  • Experience with data pipeline tools such as Apache Airflow, Kubeflow, or similar
  • Strong problem-solving skills and ability to work in fast-paced, collaborative environments.
  • Education / Certifications :

  • Bachelor's degree in computer science, Information Management or related field.
  • Preferred Skills :

  • Experience with MLOps platforms such as MLflow, Weights & Biases, Neptune
  • Knowledge of streaming data processing such as Kafka, Kinesis
  • Familiarity with infrastructure monitoring tools such as Prometheus, Grafana
  • Understanding of model interpretability and explainability techniques
  • Experience with feature stores and data versioning tools
  • Certification in cloud platforms such as AWS ML, Azure AI, GCP ML
  • (ref : hirist.tech)

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