Job Details:

Job Description:

About Altera

At Altera™, our independence as the world’s largest pure‑play FPGA solutions provider gives us the focus, speed, and agility to innovate without compromise. With more than four decades of industry‑leading FPGA expertise, our singular mission is to deliver the programmable technologies that help customers differentiate, innovate, and scale across rapidly evolving markets like AI, cloud, networking, and edge. As an independent company, we move faster, invest deeper, and partner more closely—empowering our teams to drive breakthrough innovation and shape the future of the FPGA industry.

About the Role

We are looking for a Senior MLOps & AI Infrastructure Engineer to architect, build, and operationalize machine learning systems at scale. This role sits at the intersection of data science, software engineering, and infrastructure — combining deep ML expertise with the DevOps/MLOps discipline required to ship models reliably into production.

You will partner closely with software, data, and infrastructure teams to design end-to-end ML pipelines, automate model lifecycle management, and deliver AI-powered capabilities across our EDA, HPC, and cloud environments.

Key Responsibilities:

ML Platform & Pipeline Engineering

•    Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment across cloud and on-prem HPC environments

•    Build MLOps infrastructure including experiment tracking, model registry, feature stores, and automated retraining workflows

•    Implement CI/CD/CT (Continuous Training) pipelines for ML models using tools such as Kubeflow, MLflow, Airflow, or similar

•    Containerize ML workloads with Docker and orchestrate at scale using Kubernetes and GPU node pools

Model Development & Optimization

•    Develop, fine-tune, and deploy large-scale models including LLMs, GNNs, and reinforcement learning agents for EDA and chip design applications

•    Apply advanced techniques: transfer learning, quantization, pruning, distillation, and RLHF for production-grade model efficiency

•    Implement A/B testing frameworks and shadow deployments for safe model rollout

•    Benchmark and optimize model inference performance on GPU/TPU clusters

Data Engineering & Feature Management

•    Build and maintain data pipelines for large-scale structured and unstructured datasets (terabyte-scale)

•    Collaborate with data teams to design feature engineering systems and maintain data quality for ML training

•    Implement data versioning and lineage tracking (DVC, Delta Lake, or similar)

Infrastructure & Operations

•    Manage cloud ML infrastructure on AWS (SageMaker), Azure (AML), or GCP (Vertex AI) with cost and performance optimization

•    Automate infrastructure provisioning using Terraform or CloudFormation for GPU-backed ML environments

•    Build monitoring, alerting, and observability systems for model performance drift, data quality, and system health

•    Support HPC schedulers (LSF, Slurm) for large-scale distributed training jobs

Collaboration & Leadership

•    Partner with research scientists to productionize experimental models with engineering rigor

•    Mentor junior engineers and define ML engineering best practices across the organization

•    Drive adoption of AI/ML solutions within semiconductor, EDA, and simulation workflows

Technology Stack

ML Frameworks:

PyTorch • TensorFlow • JAX • Hugging Face • scikit-learn • XGBoost

MLOps & Pipelines:

MLflow • Kubeflow • Airflow • Weights & Biases • DVC • Feast

Infrastructure & Cloud:

AWS SageMaker / GCP Vertex AI / Azure ML • Terraform • Docker • Kubernetes • Slurm / LSF

Languages:

Python • Bash • Go • SQL

Monitoring & Observability:

Prometheus • Grafana • ELK Stack • Evidently AI • Arize

Key Competencies

•    Strong ownership mindset — you drive ML initiatives from prototype to production without being asked

•    Bias toward automation: