AI Platform Engineer

Job description

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do. Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions.

We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. We are looking for an Artificial Intelligence (AI) Platform Engineer to join our industry-leading data and IP management product team to build the Machine Learning Operations ( MLOps ) infrastructure that powers SOS AI, our AI lifecycle management platform that brings together Electronic Design Automation (EDA) and AI/ML workflows.

Responsibilities

Design, build, and operate the SOS AI platform: the orchestration, tracking, messaging, and application services that run the AI/ML lifecycle on-premises. Build experiment orchestration as versioned, reproducible workflows, with run context, task dependencies, execution tracking, and reliable replay. Implement asset registries and end-to-end lineage so every model, dataset, and result is traceable to the exact inputs, parameters, and workflow version that produced it.

Package and deploy the platform as a self-contained system that runs reliably in customer environments without dependence on external services. Integrate the platform with engineering and EDA workflows so that data flows cleanly between tools and the experiment substrate.

Own platform reliability and performance

observability, scaling, upgrade paths, and operational tooling for the backend services. Establish access control, audit trails, and reproducibility guarantees appropriate for IP-sensitive engineering data. Collaborate with ML engineers, product, and customers to turn AI/ML workflow needs into durable platform capabilities.

Qualifications

BS, MS, or Ph.D. in Computer Science, Engineering, or related field 5+ years building and operating AI or data platforms in production. Hands-on experience with workflow orchestration, particularly Prefect (or comparable orchestrators such as Airflow or Dagster ). Strong containerization and orchestration skills (Docker and Kubernetes) for building and deploying distributed backend services. Solid backend engineering in Python, including API design, relational databases (PostgreSQL), and service architecture.

Experience with MLOps tooling such as experiment tracking and model registries (for example MLflow ), and with messaging or eventing systems (for example NATS or Kafka). Experience delivering on-premises or air-gapped systems, with a focus on reliability and operability. Experience with high-performance computing (HPC) environments and job schedulers is valued. Experience in semiconductor, EDA, or other engineering domains is a plus.

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