MLOps Engineer

18 hours ago


Bengaluru, India QUARKS TECHNOSOFT PRIVATE LIMITED Full time

Description : We are seeking a hands-on MLOps Engineer with 36 years of experience in machine learning infrastructure, automation, and deployment.You will design and manage scalable, production-grade ML systems with a strong focus on Docker, Kubernetes, CI/CD, and cloud environments.While this role primarily emphasizes MLOps and DevOps engineering, a solid understanding of machine learning workflows and exposure to Generative AI (GenAI) or Large Language Models (LLMs) is a strong plus.Key Responsibilities : - Design, build, and maintain end-to-end MLOps infrastructure for model training, deployment, and monitoring.- Develop and manage CI/CD pipelines for ML workflows using Docker and Kubernetes.- Automate model deployment, data pipeline orchestration, and monitoring for ML systems in production.- Collaborate with data scientists and ML engineers to operationalize ML models and ensure robust production performance.- Implement Infrastructure-as-Code (IaC) using tools like Terraform, Helm, or Cloud Deployment Manager.- Set up monitoring, logging, and alerting for model performance, drift, and system health.- Optimize resource utilization and ensure high scalability and reliability across distributed ML workloads.- (Good to have) Support GenAI/LLM-based applications, including RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.Key Requirements : Core Skills : - 3 to 6 years of hands-on experience in MLOps .- Strong proficiency in Python and scripting for automation and tooling.- Deep understanding of the ML lifecycle from data preparation to model deployment and monitoring.- Expertise with Docker (containerization) and Kubernetes (orchestration).- Experience with CI/CD pipelines and tools like GitHub Actions, Jenkins, ArgoCD, or GitLab CI.- Familiarity with MLOps platforms such as MLflow, Kubeflow, Airflow, or Vertex AI Pipelines.- Experience with cloud platforms GCP preferred, AWS or Azure acceptable.- Working knowledge of monitoring and observability tools (Prometheus, Grafana, ELK, or Cloud Monitoring). (ref:hirist.tech)


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