MLOps Engineer

2 days ago

India EROS GenAI Full time

Role Overview We are looking for an experienced MLOps Engineer to build and scale our AI infrastructure across Kubernetes, cloud-native environments, and serverless GPU platforms. You will own the end-to-end operational lifecycle of machine learning models—from training to deployment, monitoring, optimization, and automated retraining. Key Roles • Design and implement highly scalable AI/ML infrastructure using Kubernetes, Kubeflow, Ray, and cloud-native services. • Build robust CI/CD and CT (Continuous Training) pipelines for model deployment, inference, monitoring, and automated retraining. • Architect and deploy ML workflows on serverless GPU platforms (AWS, GCP, Yotta, RunPod, Modal, etc.) for cost-efficient, elastic scaling. • Establish automated systems for model drift, data drift, performance monitoring, and lineage tracking. • Promote best practices in reproducible ML, infrastructure-as-code, automation, and internal tooling. Responsibilities • Evaluate, integrate, and optimize MLOps tools (MLflow, Weights & Biases, KServe, Seldon, BentoML, Argo, Airflow, etc.) to streamline AI development. • Develop scalable inference-serving layers—batch, real-time, streaming—using GPU-optimized serving frameworks. • Build observability stacks for GPU utilization, latency, throughput, and model health metrics. • Implement robust systems for model governance, versioning, rollout strategies (blue/green, canary), and automated rollback. • Collaborate closely with ML engineers, data engineers, and product teams to deliver production-ready AI features. Knowledge & Skills Requirements • Strong understanding of ML/DL fundamentals and hands-on experience with model training and optimization. • Expertise in Kubernetes, containerization, Helm, and cloud-native infrastructure. • Experience with serverless GPU architectures and distributed computing frameworks. • Solid knowledge of CI/CD tools (GitHub Actions, GitLab CI, Jenkins), IaC (Terraform), and workflow engines. • Understanding of drift detection, performance tracking, experiment management, and scalable model deployment patterns. We Accept International Applicants

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