Senior MLOps & AI Infrastructure Engineer
Core
Architect, build, and operationalize machine learning systems at scale for EDA, HPC, and cloud environments, bridging data science, software engineering, and infrastructure.
Role type
Senior MLOps & AI Infrastructure Engineer
Builds
Scalable ML pipelines, MLOps infrastructure (experiment tracking, model registry, feature stores), and AI-powered capabilities for chip design and simulation.
Domain
Semiconductor, EDA, HPC, and Cloud AI
Deliverable
production ML models
Required skills
ML pipeline engineering, MLOps infrastructure design, CI/CD/CT implementation, model development and optimization, data engineering, cloud infrastructure management, observability, mentorship
Preferred skills
LLM fine-tuning, RAG architectures, AI agent frameworks, graph neural networks, reinforcement learning, zero-trust security, large-scale simulation pipelines, EDA toolchain experience
Technologies
PyTorch, TensorFlow, JAX, Hugging Face, MLflow, Kubeflow, Airflow, Docker, Kubernetes, Terraform, AWS SageMaker, GCP Vertex AI, Azure ML, Prometheus, Grafana, ELK Stack
Responsibilities
Design and maintain scalable ML pipelines for training, evaluation, and deployment; Build MLOps infrastructure including experiment tracking and automated retraining; Develop and deploy large-scale models including LLMs and GNNs; Build and maintain data pipelines for large-scale datasets; Manage cloud ML infrastructure and automate provisioning; Partner with research scientists to productionize experimental models; Mentor junior engineers and define ML engineering best practices
Seniority
Senior, hands-on IC with mentorship responsibilities
