On-Site
Full-Time
Chennai, Tamil Nadu
India
About the Role
Role: ML Ops Engineer
Mode: Onsite
Location: Chennai (Sholinganallur)
Exp: 5 years
Employment Type: Permanent (Full-time)
Key Responsibilities:
Design, build, and maintain scalable ML pipelines and deployment frameworks.
Manage the full lifecycle of machine learning models, including versioning, monitoring, and rollback strategies.
Develop and maintain cloud-based infrastructure across GCP, AWS, and Azure for ML workloads.
Automate deployment processes using CI/CD tools such as Jenkins or GitLab CI/CD.
Containerize applications using Docker and orchestrate with Kubernetes.
Integrate and manage ML platforms and tools like MLflow, TensorFlow, and Cubeflow.
Collaborate with Data Scientists and DevOps teams to ensure robust and reliable production systems.
Monitor model performance and implement alerting systems for model drift or degradation.
Optimize resource usage and ensure cost-effective operations across cloud environments.
Required Skills & Qualifications:
5+ years of experience in MLOps or related DevOps roles with machine learning systems.
Hands-on expertise with GCP, AWS, and Azure cloud platforms.
Proficiency in Python and experience with ML libraries (e.g., TensorFlow, Scikit-learn).
Strong experience with Docker and Kubernetes.
Experience implementing CI/CD pipelines using tools like Jenkins, GitLab, or similar.
Familiarity with MLflow, TensorFlow Serving, and other ML model management tools.
Solid understanding of cloud infrastructure automation and IaC practices (e.g., Terraform, CloudFormation).
Must have Skills:
Strong experience in any two cloud technologies (Azure, AWS, GCP)
Interested candidates can reach us at @7338773388 or [email protected], & [email protected]
Mode: Onsite
Location: Chennai (Sholinganallur)
Exp: 5 years
Employment Type: Permanent (Full-time)
Key Responsibilities:
Design, build, and maintain scalable ML pipelines and deployment frameworks.
Manage the full lifecycle of machine learning models, including versioning, monitoring, and rollback strategies.
Develop and maintain cloud-based infrastructure across GCP, AWS, and Azure for ML workloads.
Automate deployment processes using CI/CD tools such as Jenkins or GitLab CI/CD.
Containerize applications using Docker and orchestrate with Kubernetes.
Integrate and manage ML platforms and tools like MLflow, TensorFlow, and Cubeflow.
Collaborate with Data Scientists and DevOps teams to ensure robust and reliable production systems.
Monitor model performance and implement alerting systems for model drift or degradation.
Optimize resource usage and ensure cost-effective operations across cloud environments.
Required Skills & Qualifications:
5+ years of experience in MLOps or related DevOps roles with machine learning systems.
Hands-on expertise with GCP, AWS, and Azure cloud platforms.
Proficiency in Python and experience with ML libraries (e.g., TensorFlow, Scikit-learn).
Strong experience with Docker and Kubernetes.
Experience implementing CI/CD pipelines using tools like Jenkins, GitLab, or similar.
Familiarity with MLflow, TensorFlow Serving, and other ML model management tools.
Solid understanding of cloud infrastructure automation and IaC practices (e.g., Terraform, CloudFormation).
Must have Skills:
Strong experience in any two cloud technologies (Azure, AWS, GCP)
Interested candidates can reach us at @7338773388 or [email protected], & [email protected]