MLOps Engineer

About The Role

The role owns the infrastructure, orchestration, and scaling of machine learning systems, ensuring models transition smoothly from research notebooks to high-availability production services. The engineering team builds robust MLOps pipelines, monitoring frameworks, and automated deployment tooling to support high-throughput AI workloads.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines using Kubernetes, Docker, Kubeflow, and MLflow for automated model training, validation, and deployment
• Provision and manage scalable cloud infrastructure on AWS or GCP to support intensive distributed model training and low-latency inference workloads
• Build robust data and feature pipelines to ensure low-latency data access and integrity across training and serving environments
• Implement comprehensive model monitoring systems for real-time detection of data drift, concept drift, and performance degradation
• Optimize model inference latency, throughput, and resource utilization through quantization, pruning, and hardware acceleration techniques
• Collaborate with machine learning engineers and data scientists to establish standardized CI/CD pipelines for model artifacts and code

What We Are Looking For

• 3–7 years of experience in MLOps, DevOps, or machine learning engineering with a strong focus on production infrastructure
• Deep expertise in containerization and orchestration tools: Docker, Kubernetes, and Helm
• Hands-on experience with ML lifecycle platforms and model registries such as MLflow, Weights & Biases, Kubeflow, or SageMaker
• Solid understanding of CI/CD principles and automated testing for both software and machine learning models
• Bonus: Experience deploying and scaling Large Language Models (LLMs) and vector databases in production environments