MLOps Engineer

Role Overview

Evlo AI is hiring an MLOps Engineer to own the infrastructure, automation, and scaling of machine learning workflows. You’ll help transition models from research prototypes to reliable production services by bridging data science and software engineering.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for automated model training, validation, packaging, and deployment
• Implement containerized model serving using Docker, Kubernetes, and Triton Inference Server or FastAPI
• Set up monitoring for model performance, data drift, concept drift, and system resource utilization
• Manage feature stores and automated data pipelines to keep training features consistent with real-time inference
• Collaborate with ML engineers and data scientists to optimize latency, throughput, and cloud cost
• Enforce security, governance, and reproducibility standards across ML artifacts and experiments

Requirements

• 3–6 years of experience in MLOps, DevOps, or ML engineering, focused on production infrastructure
• Strong expertise in Kubernetes and cloud platforms (AWS, GCP, or Azure)
• Hands-on experience with ML lifecycle/orchestration tools: MLflow, Kubeflow, Airflow, or Metaflow
• Proficiency in Python, Infrastructure as Code (Terraform/CloudFormation), and CI/CD (GitHub Actions, GitLab CI)
• Understanding of networking, security, and performance tuning for high-throughput, low-latency ML services

Nice to Have

• Experience deploying and scaling LLMs / GenAI in production