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.
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
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.
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.
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
