Job Description Summary:
We are seeking a skilled Machine Learning Ops Engineer with 3–5 years of experience to design, deploy, and maintain scalable machine learning systems in production. The ideal candidate will have hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn, along with robust expertise in cloud platforms (AWS, Azure, or GCP) and containerization technologies like Docker and Kubernetes.

Job Description
Key Responsibilities:
Design, implement and maintain ML pipelines for model training, validation, and deployment
Automate model training, testing and deployment (CI/CD of ML models)
Monitor model performance, data drift, and system health in production environments.
Deploy models in pre-prod, prod 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):
3 -5 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
Robust problem-solving skil

📌 Software Engineer Bengaluru
🏢 Netsmart Technologies
📍 Bengaluru