MLOps Engineer

About The Role

The role is responsible for bridging the gap between machine learning model development and production-grade software engineering. This position focuses on building, maintaining, and scaling the infrastructure and CI/CD pipelines that allow machine learning models to run reliably, securely, and efficiently in production environments.

The team works closely with data scientists, backend developers, and data engineers to establish robust MLOps practices, automate deployment lifecycles, and monitor model performance, ensuring that model drift and latency anomalies are resolved before impacting users.

Key Responsibilities

• Design, build, and maintain robust CI/CD pipelines for machine learning models, ensuring automated testing, packaging, and versioned deployment to production
• Implement and manage automated model monitoring, logging, and alerting systems to track data drift, concept drift, and model latency metrics
• Optimize model serving infrastructure using Kubernetes, Triton Inference Server, or TorchServe to support high-throughput, low-latency API endpoints
• Develop and maintain feature store integrations and orchestration pipelines using tools such as Feast, Apache Airflow, or Prefect
• Build automated retraining pipelines to continuously update production models with minimal downtime and risk
• Collaborate with security and compliance teams to ensure data governance, privacy, and lineage tracking throughout the ML lifecycle

What We Are Looking For

• 3-6 years of experience in software engineering, DevOps, or data engineering, with at least 2 years dedicated to MLOps in production environments
• Strong proficiency in Python and shell scripting, alongside hands-on experience with containerization technologies like Docker and Kubernetes
• Deep experience with at least one major cloud platform (AWS, GCP, or Azure) and its respective ML deployment services
• Experience with ML tracking and registry tools such as MLflow, Weights \& Biases, or Kubeflow
• Solid understanding of infrastructure as code (IaC) principles using Terraform or CloudFormation
• Bonus: Experience deployment-testing Large Language Models (LLMs) or managing distributed training infrastructure using Ray or Spark