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 engineeringProficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, Scikit-learnExperience with containerization (Docker) and orchestration tools (Kubernetes)Knowledge of cloud platforms such as AWS, Azure, GCP and their ML servicesUnderstanding of CI / CD pipelines, version control (Git), and infrastructure as codeFamiliarity with monitoring tools and logging frameworks for production systemsExperience with data pipeline tools such as Apache Airflow, Kubeflow, or similarStrong 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, NeptuneKnowledge of streaming data processing such as Kafka, KinesisFamiliarity with infrastructure monitoring tools such as Prometheus, GrafanaUnderstanding of model interpretability and explainability techniquesExperience with feature stores and data versioning toolsCertification in cloud platforms such as AWS ML, Azure AI, GCP ML(ref : hirist.tech)