Machine Learning Engineer at Keysight Technologies in Singapore

Job description

Overview Keysight is a mission-critical design enablement partner for organizations solving the world’s most complex engineering challenges. With deep engineering expertise and leading design, emulation, and test solutions, our ~16,800 employees help customers in over 100 countries accelerate innovation across communications, automotive, energy, quantum, aerospace and defense, semiconductor, and artificial intelligence markets. Learn more about what we do . At Keysight, people come together to explore new ideas, challenge what's possible, and advance the technologies defining the future. Here, you'll find opportunities to do your best work, take your career further, and thrive in an award-winning culture . Learn more about our benefits . Responsibilities We are seeking an experienced AI/ML Engineer to lead the design, development, and scaling of advanced AI/ML solutions across our analytics platform in the manufacturing and semiconductor sectors. This high-impact role combines deep expertise in classical machine learning with cutting-edge Generative AI capabilities to deliver production-grade systems for anomaly detection, predictive maintenance, market intelligence, automated test plan generation, and expert-level customer support. You will own end-to-end AI/ML initiatives — from numerical sensor/test data modeling to unstructured text processing and LLM-powered workflows — in a high-stakes, regulated industrial environment where precision, reliability, hallucination mitigation, and risk minimization are mandatory. This is a hands-on senior position requiring both architectural knowledge and strong implementation skills. Key Responsibilities Lead the architecture and continuous improvement of unified AI/ML capabilities, integrating classical ML models with Generative AI platforms (primarily AWS Bedrock) to support mission-critical applications in semiconductor manufacturing and risk analytics. Design and implement robust anomaly detection and predictive maintenance systems using classical ML algorithms (XGBoost, Scikit-learn) on real-time sensor and test data, while incorporating drift detection and model monitoring to maintain long-term reliability. Build and scale RAG pipelines and agentic workflows for high-precision tasks, including automated generation of manufacturing test plans from historical test data/measurement instrument records, with strong emphasis on accuracy, hallucination reduction, and risk controls. Develop intelligent summarization and information extraction pipelines that process thousands of scraped news articles, press releases, and open-source intelligence into concise, actionable market intelligence reports, leveraging techniques such as intelligent chunking, semantic filtering (embeddings + k-NN), map-reduce patterns, TF-IDF augmentation, and agentic orchestration. Own the development and maintenance of a customer-facing GenAI Q&A chatbot that provides deep, domain-specific insights into semiconductor manufacturing risks based on sensor measurements and test plans. Tackle diverse classical ML problems (regression, classification, clustering, time-series forecasting) and integrate them with GenAI components when hybrid approaches deliver better outcomes. Apply NLP techniques — including classical recurrent architectures (RNNs/LSTMs) and modern LLM-based methods — to extract insights from unstructured sources (market reports, operational logs, competitor pricing data). Collaborate with MLOps, data engineering, domain experts, and product teams in an Agile/Scrum environment to iterate models, conduct rigorous validation, ensure CI/CD, observability, versioning, and automated testing for all AI components. Perform advanced model evaluation, hyperparameter tuning, feature engineering, bias/risk assessment, and ethical AI practices, with particular attention to imbalanced datasets, concept/data drift monitoring, and production reliability. Contribute to large-scale data pipeline enhancements using tools li