MLOps Engineer

About The Role

The role owns the infrastructure, orchestration, and deployment pipelines that power machine learning at scale, ensuring models move reliably from research to production.

The team works at the intersection of software engineering and machine learning to build resilient platforms that handle continuous integration, monitoring, and scaling for complex AI workloads.

Key Responsibilities

• Design and build robust MLOps infrastructure using Kubernetes, Docker, Terraform, and cloud-native tools
• Implement end-to-end CI/CD pipelines specifically tailored for machine learning model training, testing, and deployment
• Manage and optimize model serving layers using platforms like Triton Inference Server, TorchServe, or vLLM to maintain low latency and high throughput
• Configure automated monitoring systems to detect data drift, concept drift, and system performance anomalies in production models
• Collaborate with data scientists and ML engineers to containerize models and streamline feature store integrations
• Establish security, compliance, and governance best practices for model artifacts, datasets, and infrastructure
  
  

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or reliability engineering, with a focus on machine learning infrastructure
• Deep proficiency in Python, containerization (Docker), and container orchestration (Kubernetes)
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools like Terraform
• Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or SageMaker
• Solid understanding of CI/CD principles, logging, monitoring, and distributed systems
• Bonus: Experience managing LLM serving infrastructure or vector databases at scale