Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 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 seeking a highly skilled and experienced Senior Machine Learning Engineer to play a pivotal role in designing, developing, and scaling our company's unified Generative AI platform. This strategic initiative powers mission-critical applications in the manufacturing and semiconductor industries, where precision, reliability, and risk mitigation are non-negotiable.

You will collaborate closely with MLOps engineers, data scientists, product teams, and domain experts to build production-grade GenAI solutions that leverage large language models (LLMs), advanced retrieval-augmented generation (RAG), and emerging agentic architectures. Our platform drives tangible business value through automated test plan generation, market intelligence summarization, and expert-level customer support for our flagship analytic product.

This is a high-impact, hands-on position for someone who thrives on solving complex, domain-specific challenges in regulated industrial environments.

• Lead the architecture, development, and continuous enhancement of the company's core unified Generative AI platform, built primarily on AWS Bedrock.
• Design and implement robust RAG pipelines for high-precision applications, with a strong emphasis on accuracy, hallucination mitigation, and risk minimization — particularly in the generation of manufacturing test plans from vast historical datasets of test plans and measurement instrument data.
• Experience in model lifecycle monitoring using any Cloud tools, to detect concept and data drift of existing model deployed.
• Develop intelligent workflows to ingest, process, and distill thousands of scraped news articles, press releases, and open-source intelligence into concise, actionable market intelligence summaries (typically reducing input to 12–48 highly relevant documents), utilizing advanced techniques such as intelligent chunking, semantic relevance filtering (e.g., embeddings + k-NN), map-reduce summarization patterns, TF-IDF augmentation, or AI agentic orchestration when superior to traditional methods.
• Build and maintain a customer-facing Q&A chatbot for our proprietary products, enabling users to query and gain deep insights into semiconductor manufacturing risk identification based on sensor measurements and test plan data.
• Collaborate with MLOps engineers to ensure full end-to-end reliability, observability, versioning, automated testing, and CI/ CD for all GenAI components in production.
• Contribute to prompt engineering, knowledge base curation, vector database optimization (e.g., embeddings tuning, hybrid search), Lambda function development, and Bedrock custom model/ Agent workflows.
• Partner cross-functionally in an Agile environment, participating actively in sprint planning, backlog refinement, technical design reviews, and iterative delivery to meet demanding timelines and quality standards.
• Stay abreast of the latest advancements in GenAI, RAG, agentic systems, and responsible AI practices, and proactively propose innovations that enhance platform capabilities and business outcomes.

Must-have qualifications

• Master's degree in Machine Learning, Computer Science, Quantitative Mathematics, Statistics, or a closely related field.
• 1–3 years of professional experience as a Machine Learning Engineer/ Data Scientist, with proven track record of independently owning end-to-end development, training, validation, and production deployment of ML/ GenAI models.
• Hands-on expertise in Generative AI and RAG architectures, including practical experience with AWS Bedrock, Knowledge Bases, custom model fine-tuning, prompt engineering, and Lambda function and Step functions for orchestration of GenAI workloads.
• Demonstrated experience building scalable summarization or information extraction pipelines handling very large document sets (thousands of articles), using map-reduce, agentic patterns, or advanced filtering techniques.
• Solid software engineering foundation: production-grade Python, clean code practices, testing, version control (Git), and CI/ CD.
• Familiarity with Agile/ Scrum methodologies and experience delivering iteratively in sprint-based environments.
• Strong quality mindset with background in QA/ validation of ML systems, including rigorous evaluation metrics, bias/ risk assessment, and reliability engineering.

Strongly preferred

• Fluency in English and all of its technical terms.
• Prior domain exposure to manufacturing, semiconductor processes, measurement/ test instrumentation, or risk analytics.
• Hands-on experience with agentic AI frameworks, multi-agent systems, and tools such as LangChain/ LangGraph, CrewAI, or Bedrock Agents.
• Practical knowledge of Apache Spark for large-scale data processing, as well as associated databases and distributed computing patterns.
• Experience with MLOps best practices, model monitoring, drift detection, and automated retraining pipelines.

If you are a proactive, detail-oriented engineer passionate about delivering precise, high-stakes Generative AI solutions in industrial contexts, we invite you to bring your expertise to our team and help shape the future of intelligent manufacturing analytics.

We offer a collaborative, innovation-driven environment where your contributions will have direct, visible impact on product excellence and customer success.

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