Gurugram, Haryana, India
Information Technology
Full-Time
mokSa.ai
Overview
Job Overview
We are seeking an experienced MLOps Cloud Engineer to join our dynamic team. In this role, you will be responsible for designing, deploying, and maintaining scalable cloud infrastructure for machine learning workloads. You will collaborate with data scientists, machine learning engineers, and software developers to ensure smooth and efficient integration of ML models into production systems.
Key Responsibilities
We are seeking an experienced MLOps Cloud Engineer to join our dynamic team. In this role, you will be responsible for designing, deploying, and maintaining scalable cloud infrastructure for machine learning workloads. You will collaborate with data scientists, machine learning engineers, and software developers to ensure smooth and efficient integration of ML models into production systems.
Key Responsibilities
- Design, deploy, and manage cloud infrastructure to support scalable and reliable ML workflows.
- Collaborate with ML engineers and data scientists to understand model requirements and operationalize ML pipelines.
- Build and maintain CI/CD pipelines to automate training, testing, and deployment of ML models.
- Monitor and manage the performance, reliability, and health of deployed models using alerting systems and performance dashboards.
- Optimize cloud resource usage to ensure cost-effectiveness and performance.
- Implement security best practices to safeguard ML models and sensitive data in the cloud.
- Work with software teams to seamlessly integrate ML models into production applications.
- Troubleshoot and resolve infrastructure, deployment, and performance-related issues.
- Stay current with the latest trends in cloud computing, DevOps, and ML operations.
- Document infrastructure architecture, deployment processes, and operational procedures.
- Bachelors degree in Computer Science, Engineering, or a related field.
- 3+ years of experience working with cloud platforms (especially AWS, GCP) and DevOps tools.
- Proficiency in scripting languages such as Python and Shell.
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Familiarity with common machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Strong analytical and problem-solving skills, with the ability to thrive in a fast-paced environment.
- AWS, GCP and Kubernetes certifications.
- Experience with infrastructure-as-code tools (e.g., Terraform, Ansible).
- Familiarity with ML model deployment platforms such as MLflow or Kubeflow.
- Understanding of cloud security and cybersecurity principles.
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