Machine Learning Engineer
Core
Build reliable ML pipelines, improve deployment processes, and ensure engineering best practices across the machine learning lifecycle for AI/ML product development.
Role type
Semi Senior Machine Learning Engineer (MLOps)
Builds
Scalable AI/ML products and cloud-native ML solutions
Domain
Artificial Intelligence / Machine Learning / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, ML pipeline orchestration (Kubeflow, Airflow, MLflow), containerization (Docker, Kubernetes), CI/CD automation, code optimization, cloud ML deployment (AWS SageMaker, GCP AI Platform)
Preferred skills
Infrastructure as Code, model monitoring, feature stores, LLMOps
Technologies
Kubeflow, Airflow, MLflow, Docker, Kubernetes, GitLab CI, Jenkins, Azure Repos, AWS SageMaker, GCP AI Platform
Responsibilities
Design and maintain scalable ML pipelines; Support deployment, monitoring, and optimization of ML models; Build production-grade Python services and reusable components; Containerize and orchestrate ML workloads; Collaborate with data science teams to transition experiments to production; Optimize CI/CD workflows for ML applications; Contribute to infrastructure setup and automation for AI platforms
Seniority
Semi Senior, hands-on IC
