MLOps Engineer (CX)
Core
Architect and manage scalable cloud infrastructure workloads, translate experimental models into production-ready systems, and design end-to-end ML pipelines.
Role type
MLOps Engineer
Builds
Production ML pipelines, scalable data infrastructure, and cloud infrastructure workloads
Domain
Cloud Computing / Machine Learning Operations
Deliverable
production ML models | infrastructure
Required skills
Python (FastAPI, Flask), Cloud platforms (AWS, GCP, Azure), Kubernetes (EKS/GKE/AKS), Docker, CI/CD pipelines, Infrastructure as Code, Apache Spark, Distributed computing, Version control, Async programming, Concurrent system design
Preferred skills
PyTorch, scikit-learn, numpy, MLflow, Kubeflow, vLLM, SGLang, Ray.io, Label Studio, SageMaker, Vertex AI
Technologies
FastAPI, Flask, AWS, GCP, Azure, Kubernetes, Docker, Apache Spark, MLflow, Kubeflow, vLLM, SGLang, Ray.io, Label Studio, SageMaker, Vertex AI
Responsibilities
Architect and manage scalable cloud infrastructure workloads including container orchestration and automated testing; Partner with data scientists to translate experimental models into robust production systems; Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring; Design and implement scalable data infrastructure solutions leveraging distributed computing frameworks.
Seniority
Mid-Senior (5+ years experience)