AI Software Solutions Engineer

Keysightis 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 moreabout 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 seeking an AI Software Solutions Engineer to join our Digital and Photonics Software R&D team. This role combines AI components with high-performance instrument software to create AI-powered solutions. It calls for expertise in Digital and Photonics measurements and familiarity with - and a strong desire to learn - Generative AI, Agentic (multi-agent) AI, and MLOps. You will work on a team of engineers to deliver production-ready AI features for Digital and Photonics Measurement products.

• Partner with the Solutions Architect to shape and review the end-to-end AI solution architecture for intelligent Digital and Photonics Measurement platforms, covering Generative AI, Agentic AI, and inference-driven workflows.
• Use measurement domain knowledge to implement AI-powered solutions for Sampling or Realtime oscilloscopes, BERTs, AWGs, etc.
• Build multi-agent systems and custom orchestration layers, aligning with the Solutions Architect on agent roles, tool calls, memory, and reasoning loops.
• Implement secure AI data flows: ingestion, preprocessing, feature extraction, model inference, post-processing, and feedback loops, ensuring data integrity and traceability.
• Help evaluate and incorporate emerging AI technologies (e.g., RAG architectures, multimodal models, small language models for edge deployment) into the platform roadmap.

• Serve as a senior member of a team of Digital and Photonics Measurement domain experts and AI engineers; help plan, prioritize, and track tasks to ensure timely delivery of milestones.
• Foster a culture of experimentation, rigorous AI evaluation, and responsible AI development.
• Help Project Managers track progress using continuous-improvement methods and AI-driven delivery metrics.

• Partner with the Project Manager and Solutions Architect to help plan, sequence, and execute AI projects from design through delivery.
• Partner with Product Management to translate measurement and analysis requirements into AI solution designs.
• Coordinate with QA, validation, and compliance teams to ensure AI outputs are reliable, explainable, and auditable.

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, or a related field.
• Measurement domain knowledge of Sampling or Realtime oscilloscopes, BERTs, AWGs, etc.
• 8+ years of software development experience, with at least 3 years in a senior software development role.
• Proven mastery of modern software languages such as C++, C#, Rust, Python, and Java.
• Expertise with Git and CI/CD pipeline workflows.
• Strong foundation in software design patterns, system architecture, and performance optimization for complex multi-threaded or real-time systems.
• Demonstrated hands-on experience deploying software components in production environments.
• Familiarity with - and a strong desire to learn - AI data flow, including prompt engineering, retrieval-augmented generation (RAG), tool/function calling, and multi-step agent reasoning.

• Experience deploying AI/ML models on edge or embedded platforms, including model quantization, pruning, and on-device inference optimization.
• Hands-on experience with agentic frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar orchestration toolkits.
• Knowledge of hardware/software integration, FPGA data pipelines, or low-level driver interfaces relevant to AI-assisted signal processing.
• Familiarity with AI governance frameworks, explainability tooling (SHAP, LIME, attention visualization), and responsible AI practices.
• Strong communication skills with the ability to articulate complex AI architectural decisions to diverse audiences.

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