MLOps Engineer

About The Role The role bridges the gap between machine learning research and production software engineering, owning the infrastructure, tooling, and pipelines required to deploy and monitor models at scale. The engineer will focus on building reliable, automated MLOps platforms that support continuous training, deployment, and monitoring of both traditional ML models and modern LLM applications. Working closely with data scientists, backend developers, and data engineers, the role ensures that model inference pipelines are highly available, latency‑optimized, and resilient to failures. The position demands a deep understanding of cloud infrastructure, container orchestration, and automated CI/CD workflows tailored specifically for machine learning. Key Responsibilities Design, build, and maintain robust CI/CD pipelines for ML model deployment using tools like GitLab CI, GitHub Actions, and Argo Workflows. Implement and manage scalable model serving infrastructure on Kubernetes (EKS/GKE) utilizing KServe, Triton Inference Server, or Seldon Core. Develop automated pipelines for continuous training, model evaluation, and artifact versioning using MLflow, DVC, or Feast feature stores. Set up comprehensive monitoring and alerting systems to track model performance, data drift, and system metrics in production with Prometheus, Grafana, and Arize. Optimize inference latency and infrastructure costs through model quantization, caching strategies, and efficient GPU/CPU resource allocation. Collaborate with security and compliance teams to enforce data governance, model lineage tracking, and secure access controls across the ML lifecycle. What We Are Looking For 3–6 years of experience as an MLOps Engineer, DevOps Engineer, or Software Engineer specializing in machine learning infrastructure. Strong proficiency in Python and solid experience with containerization technologies, specifically Docker and Kubernetes. Hands‑on experience with Infrastructure as Code (IaC) tools like Terraform or Pulumi to manage cloud resources (AWS, GCP, or Azure). Deep understanding of modern MLOps frameworks, including MLflow, Kubeflow, Weights & Biases, or similar orchestrators. Solid software engineering fundamentals, including REST/gRPC API design, microservices architecture, and automated testing. Bonus: Experience deployment‑optimizing large language models (LLMs) using vLLM, TensorRT‑LLM, or Ray, or certifications in AWS/GCP cloud architecture. #J-18808-Ljbffr