MLOps Engineer

About the job About The Role The role focuses on building, scaling, and maintaining the infrastructure that powers machine learning models in production. This engineer bridges the gap between data science and software engineering, ensuring that models are deployed reliably, monitored continuously, and scaled efficiently to meet production demands. The team operates at the intersection of infrastructure engineering and applied machine learning. The role requires a deep understanding of CI/CD pipelines, container orchestration, cloud-native architectures, and data engineering to automate the entire machine learning lifecycle from training to real-time inference. Key Responsibilities Design and implement robust CI/CD pipelines for ML, automating model training, testing, packaging, and deployment into production environments Build and maintain orchestration pipelines using tools like Kubeflow, Airflow, or Prefect to schedule and manage complex data and ML workflows Deploy models as highly available, low-latency API endpoints using Kubernetes, Docker, and Triton Inference Server or FastAPI Implement real-time model monitoring, logging, and alerting systems to track performance, data drift, and system metrics using Prometheus and Grafana Manage and optimize feature stores (e.g., Feast or Tecton) and model registries (e.g., MLflow) to ensure consistency between training and serving data Collaborate with data scientists and platform engineers to optimize model inference speeds through quantization, pruning, or hardware acceleration (GPUs/TPUs) What We Are Looking For 3–6 years of experience as an MLOps, DevOps, or Software Engineer, with at least 2 years dedicated to deploying ML models in production Strong programming skills in Python and deep familiarity with containerization (Docker) and container orchestration (Kubernetes) Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and Infrastructure as Code (IaC) tools like Terraform Proven experience with ML tracking and deployment tools such as MLflow, Kubeflow, BentoML, or Triton Inference Server Solid understanding of software engineering best practices, including version control, unit testing, and design patterns Bonus: Experience with large-scale distributed systems, vector databases, or deploying LLMs in production environments