Lead / Principal MLOps Engineer

Nextloop Technologies LLP; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Nextloop Technologies LLP is seeking a Lead / Principal MLOps Engineer to design, build, and operate secure, scalable ML platform infrastructure. You will lead end-to-end ML workflows from training to deployment, serving, and lifecycle management, with strong emphasis on Kubernetes-based deployment and robust CI/CD pipelines.

The role requires deep hands-on expertise in ML lifecycle management, multi-cloud or hybrid-cloud environments, and collaboration with data science and platform engineering

Qualifications

• 8+ years of overall engineering experience with at least 4 years in MLOps or ML platform engineering.

Responsibilities

• Design, build, and maintain scalable MLOps and ML platform infrastructure.
• Lead end-to-end ML pipelines including training, validation, deployment, serving, monitoring, and lifecycle management.
• Build and manage Kubernetes-based model deployment and serving infrastructure.
• Implement robust CI/CD pipelines for ML applications, models, and platform infrastructure.
• Manage Infrastructure as Code and automate cloud provisioning, configuration, and environment management.
• Design and optimize Kubernetes environments for high availability, scalability, DR, security, and cost efficiency.
• Implement monitoring and observability across ML models, pipelines, applications, infrastructure, and platform services.
• Monitor model performance, data drift, concept drift, reliability, and operational health.
• Lead troubleshooting, incident management, RCA, and production issue resolution; collaborate with Data Science and Cloud teams.
• Establish MLOps best practices, governance, and documentation; provide technical leadership and reviews.

Skills

MLOps leadership

Kubernetes

CI/CD for ML

Cloud platforms

Docker

GitHub Actions / GitLab CI / Jenkins

Tools

Docker

Kubernetes

Helm

Kubeflow

MLflow

Argo Workflows

Weighs & Biases (W&B)

SageMaker

Apache Airflow

KServe

Jenkins

Job description

We are seeking a highly skilled Lead / Principal MLOps Engineer to design, build, and operate secure, scalable,and reliable enterprise ML platform infrastructure. This role will lead the end-to-end operationalization ofmachine learning workloads—from training and experimentation through deployment, serving, monitoring, andlifecycle management.

Responsibilties

• Design, build, and maintain scalable, secure, highly available, and reliable MLOps and ML platforminfrastructure.
• Lead end-to-end ML pipelines covering training, validation, deployment, serving, monitoring, and lifecyclemanagement.
• Build and manage Kubernetes-based model deployment and serving infrastructure.
• Implement robust CI/CD pipelines for ML applications, models, and platform infrastructure.
• Manage Infrastructure as Code and automate cloud provisioning, configuration, and environmentmanagement.
• Design and optimize Kubernetes environments for high availability, scalability, disaster recovery, security,and efficient resource utilization.
• Implement monitoring and observability solutions across ML models, pipelines, applications, infrastructure,and platform services.
• Monitor and manage model performance, data drift, concept drift, reliability, and operational health.
• Optimize cloud and GPU infrastructure for performance, scalability, reliability, and cost efficiency.
• Lead troubleshooting, incident management, root cause analysis, and production issue resolution.Collaborate with Data Science, ML Engineering, Platform Engineering, and Cloud teams to improve MLworkflows.
• Establish MLOps best practices, engineering standards, architecture patterns, governance controls, anddocumentation.
• Provide technical leadership, mentoring, architecture guidance, and infrastructure/code reviews..

Required Qualification

• 8+ years of overall engineering experience, including at least 4 years of hands-on experience in MLOps, MLPlatform Engineering, or Cloud Engineering.
• Strong hands-on experience designing and operating production MLOps or ML platform environments.
• Strong understanding of ML lifecycle management: experimentation, model versioning, model registry,training, deployment, serving, monitoring, and governance.
• Hands-on experience with one or more: MLflow, Kubeflow, Apache Airflow, Argo Workflows, Weights &Biases (W&B), or Amazon SageMakerAdvanced experience with Docker and Kubernetes, including EKS, GKE, or AKS.
• Strong practical experience with Helm and KServe for Kubernetes-based application and model deployment/serving.
• Strong experience with GitHub Actions, GitLab CI, or Jenkins to automate build, test, deployment, andrelease processes.
• Strong cloud-platform experience in AWS, Azure, or GCP; multi-cloud or hybrid-cloud experience ispreferred.
• Strong experience with ML pipeline orchestration, model deployment and serving, model monitoring, datadrift and concept drift detection, autoscaling, HA, DR, incident management, and RCA.
• Experience optimizing cloud and GPU infrastructure for performance, scalability, reliability, and costefficiency.
• Strong problem-solving, technical leadership, communication, stakeholder-management, and mentoringabilities.