MLOps & AI Platform Engineer
Core
Design and operate the infrastructure, tools, and workflows enabling data scientists and ML engineers to deliver reliable, scalable, and automated AI solutions (from classical ML to LLMs and generative AI) for manufacturing operations.
Role type
MLOps & AI Platform Engineer
Builds
Scalable ML lifecycle infrastructure, CI/CD pipelines, container orchestration, GPU clusters, inference servers, and vector databases for GenAI workloads.
Domain
Manufacturing / Artificial Intelligence / MLOps
Deliverable
production ML models
Required skills
Python, containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, MLflow, cloud data platforms, GPU management, inference optimization, observability practices, data security and governance
Preferred skills
Experience enabling and training AI/ML teams, building internal documentation, balancing innovation with operational stability
Technologies
MLflow, Ray, vLLM, Hugging Face TGI, Triton, GitHub Actions, Azure DevOps, Jenkins, Azure Databricks
Responsibilities
Design and maintain scalable infrastructure supporting the entire ML lifecycle; implement CI/CD pipelines and workflow automation; establish best practices for reproducibility and versioning; manage GPU clusters and optimize performance for LLMs; develop observability for ML and GenAI systems; collaborate with data scientists and AI engineers to ensure platform usability.
Seniority
Mid-to-Senior, hands-on IC
