MLOps Engineer

1 week ago

Ahmedabad India Mantra Softech Full time

Job Description Key Responsibilities - Develop and maintain CI/CD pipelines for machine learning models. - Automate model deployment, monitoring, and scaling processes. - Implement and manage version control for code, data, and models. - Ensure data quality, security, and compliance throughout the ML lifecycle. - Collaborate with data scientists, engineers, and other stakeholders. - Optimize infrastructure for ML workloads. - Troubleshoot and resolve issues in production ML systems. - Implement logging, monitoring, and alerting for ML pipelines. - Manage and optimize cloud resources for ML/AI workloads. - Facilitate knowledge sharing and best practices across teams. - Mentor peer developers on MLOps / AIOps and DevOps. - Automate workflows for multi-model deployment on servers and embedded systems. - Develop and manage APIs for serving multiple deep learning models efficiently using Flask, FastAPI, or similar. - Optimize and convert models for embedded/PC/Android/Server deployment (e.g., TFLite, ONNX, .NEF) with quantization and pruning. Required Technical Competencies - Programming languages : Python or similar. - ML frameworks : TensorFlow, PyTorch, scikit-learn. - Cloud platforms : AWS (preferred), Azure, or GCP. - DevOps tools : Docker, Kubernetes, Jenkins, GitLab CI, Azure DevOps. - Infrastructure as Code : Terraform, Ansible, CloudFormation. - Big data technologies : Spark, Hadoop, Kafka. - Monitoring and logging tools : ELK stack, Prometheus, Grafana. - Version control : Git. - Database management : SQL and NoSQL databases. - Data pipeline tools : Airflow, Kubeflow or similar. - CI/CD methodologies and tools for ML workflows. - Model serving : Flask, FastAPI, TorchServe, TensorFlow Serving. - Understanding of ML algorithms and model performance metrics. - Knowledge of data privacy and security best practices. - Familiarity with MLOps principles and tools : MLflow, DVC, Weights & Biases. (ref:hirist.tech)

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