AI Platform Engineer

Keysight Technologies; Barcelona; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Keysight Technologies is seeking an AI Platform Engineer to join the SOS AI platform team in Barcelona, tackling MLOps infrastructure that supports the AI lifecycle on-premises.

You'll design, build, and operate the platform, orchestrate experiments, ensure data provenance, and integrate with EDA workflows. Strong background in ML systems, LLMs, and GPU-enabled Python tooling is required for this challenging role.

Formación

• MS or PhD in CS, EE, or related field.
• 5+ years building production ML or data-intensive systems.
• Demonstrated experience building RAG and knowledge graph systems in production.
• Hands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.
• Strong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.
• Experience with agentic memory management and the MCP or comparable agent-grounding interfaces.
• Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face).
• ML applied to semiconductor applications, especially RF/microwave, valued.
• Familiarity with data governance, access control, and provenance in IP-sensitive environments is a plus.

Responsabilidades

• Design, build, and operate the SOS AI platform 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 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 data flows cleanly between tools and the experiment substrate.
• Own platform reliability and performance: observability, scaling, upgrade paths, and operational tooling for back-end 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.

Conocimientos

Production ML systems

RAG & knowledge graphs

LLMs on-prem inference

Vector/Graph DBs & low-latency

Agentic memory management / MCP

Python & ML frameworks (PyTorch, HF)

API design / software engineering

Educación

MS/PhD in CS/ECE or related

Herramientas

PyTorch

Hugging Face

On-prem deployment tooling

Descripción del empleo

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.

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.

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

• MS or PhD in Computer Science, Electrical Engineering, or related field
• 5+ years building production ML or data-intensive systems.
• Demonstrated experience building RAG and knowledge graph systems in production (a must): ingestion, indexing, and retrieval pipelines.
• Hands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.
• Strong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.
• Experience with agentic memory management and the Model Context Protocol (MCP) or comparable agent-grounding interfaces.
• Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.
• ML applied to semiconductor applications, especially the RF and microwave industry, is highly valued.
• Familiarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.

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