MLOps Engineer

About The Role The role drives the design, implementation, and maintenance of scalable MLOps platforms that support the entire machine learning lifecycle. The team focuses on bridging the gap between data science experimentation and robust, production-grade model deployments. You will collaborate closely with machine learning engineers and data engineers to build automated pipelines, implement continuous training loops, and establish comprehensive monitoring systems to ensure model performance, reliability, and security at scale. Key Responsibilities Build and maintain automated CI/CD pipelines for machine learning models, ensuring seamless transitions from development to production environments Design and optimize scalable orchestration workflows using tools like Kubeflow, Airflow, or Prefect to manage training and evaluation pipelines Implement and manage feature stores and model registries (such as Feast, MLflow, or Weights & Biases) for versioning, reproducibility, and tracking Deploy models as high-throughput, low-latency microservices on Kubernetes (EKS/GKE) using frameworks like Triton Inference Server, Seldon, or FastAPI Develop automated monitoring, logging, and alerting systems to track model drift, data quality, and system resource utilization in real-time Collaborate with security and compliance teams to enforce data governance, privacy standards, and secure access controls across ML infrastructure What We Are Looking For 3-6 years of experience in MLOps, DevOps, or Software Engineering, with a proven track record of deploying and managing ML models in production Strong proficiency in Python and shell scripting, along with deep experience in containerization and orchestration using Docker and Kubernetes Hands-on experience with cloud infrastructure (AWS or GCP) and Infrastructure as Code tools such as Terraform Solid understanding of software engineering best practices, including version control (Git), testing frameworks, and continuous integration tools BS or MS in Computer Science, Engineering, Mathematics, or a related technical field Bonus: Experience with vector databases, deploying LLMs/generative AI models at scale, or specialized hardware acceleration (GPUs/TPUs)