{"id":2256552,"url":"https://alion.io/job/keysight-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":53596,"name":"Keysight","domain":"keysight.com","url":"https://alion.io/company/keysight","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":46000,"max_usd":123000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-07-20T04:23:00Z","employer_posted_date":"2026-10-11","last_verified_at":"2026-10-11T17:40:26Z","board_verified":true,"closed_at":null,"days_open":83,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":83},"description":"OverviewKeysight 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.\nOur 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.\nResponsibilities\nWe 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.\nYou 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.\nLead 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.\nDesign 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.\nBuild 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.\nDevelop 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.\nOwn 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.\nTackle diverse classical ML problems (regression, classification, clustering, time-series forecasting) and integrate them with GenAI components when hybrid approaches deliver better outcomes.\nApply 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).\nCollaborate 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.\nPerform 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.\nContribute to large-scale data pipeline enhancements using tools like Apache Spark, vector databases, and distributed processing patterns.\nStay current with advancements in classical ML, GenAI (RAG, agentic systems, multi-agent frameworks), responsible AI, and industrial analytics; proactively propose innovations that drive measurable business value.\nQualifications\nMust-have qualifications\nMaster's degree in Machine Learning, Computer Science, Data Science, Statistics, Quantitative Mathematics, or a closely related field.\n4+ years of professional experience as a Machine Learning Engineer / AI Engineer (or equivalent), with a proven track record of independently owning end-to-end development, validation, and production deployment of both classical ML and GenAI/LLM-based systems.\nStrong hands-on expertise in classical ML frameworks (Scikit-learn, XGBoost) and deep learning/NLP (TensorFlow/PyTorch, RNNs/LSTMs)\nPractical experience building RAG architectures, prompt engineering, knowledge base curation, vector database optimization (embeddings tuning, hybrid search), and agentic workflows (LangChain/LangGraph, CrewAI, Bedrock Agents, or equivalent).\nDemonstrated success developing scalable summarization/information extraction pipelines for large document sets and production-grade anomaly detection/predictive models on numerical/time-series data.\nProficiency in production-grade Python, clean code practices, Git, testing, CI/CD, and MLOps best practices (model monitoring, drift detection, automated retraining).\nSolid experience with AWS Bedrock (Knowledge Bases, custom models, Lambda/Step Functions for orchestration) or comparable GenAI platforms.\nFamiliarity with Agile/Scrum, sprint-based delivery, cross-functional collaboration, and rigorous QA/validation of ML/GenAI systems (evaluation metrics, bias/risk assessment).\nFluency in English, including technical terminology.\nStrongly preferred\nDomain exposure to manufacturing, semiconductors, sensor-based analytics, test/measurement instrumentation, or industrial risk analytics.\nHands-on experience with Apache Spark for large-scale processing and distributed computing.\nPrior work integrating classical ML with GenAI (e.g., hybrid pipelines, using classical models for filtering/reranking in RAG).\nA portfolio or demonstrable projects showing innovative, production-impactful solutions combining classical ML and Generative AI in real-world settings.\nExperience with the Model Context Protocol (MCP) for building standardized, secure integrations between LLMs/agentic systems and external data sources, tools, or enterprise services (e.g., connecting to databases, APIs, or knowledge repositories in a protocol-driven rather than custom-coded manner).\nCareers Privacy Statement ***Keysight is an Equal Opportunity Employer.***\nWe 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.\nYou 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.\nLead 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.\nDesign 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.\nBuild 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.\nDevelop 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.\nOwn 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.\nTackle diverse classical ML problems (regression, classification, clustering, time-series forecasting) and integrate them with GenAI components when hybrid approaches deliver better outcomes.\nApply 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).\nCollaborate 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.\nPerform 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.\nContribute to large-scale data pipeline enhancements using tools like Apache Spark, vector databases, and distributed processing patterns.\nStay current with advancements in classical ML, GenAI (RAG, agentic systems, multi-agent frameworks), responsible AI, and industrial analytics; proactively propose innovations that drive measurable business value.\nMust-have qualifications\nMaster's degree in Machine Learning, Computer Science, Data Science, Statistics, Quantitative Mathematics, or a closely related field.\n4+ years of professional experience as a Machine Learning Engineer / AI Engineer (or equivalent), with a proven track record of independently owning end-to-end development, validation, and production deployment of both classical ML and GenAI/LLM-based systems.\nStrong hands-on expertise in classical ML frameworks (Scikit-learn, XGBoost) and deep learning/NLP (TensorFlow/PyTorch, RNNs/LSTMs)\nPractical experience building RAG architectures, prompt engineering, knowledge base curation, vector database optimization (embeddings tuning, hybrid search), and agentic workflows (LangChain/LangGraph, CrewAI, Bedrock Agents, or equivalent).\nDemonstrated success developing scalable summarization/information extraction pipelines for large document sets and production-grade anomaly detection/predictive models on numerical/time-series data.\nProficiency in production-grade Python, clean code practices, Git, testing, CI/CD, and MLOps best practices (model monitoring, drift detection, automated retraining).\nSolid experience with AWS Bedrock (Knowledge Bases, custom models, Lambda/Step Functions for orchestration) or comparable GenAI platforms.\nFamiliarity with Agile/Scrum, sprint-based delivery, cross-functional collaboration, and rigorous QA/validation of ML/GenAI systems (evaluation metrics, bias/risk assessment).\nFluency in English, including technical terminology.\nStrongly preferred\nDomain exposure to manufacturing, semiconductors, sensor-based analytics, test/measurement instrumentation, or industrial risk analytics.\nHands-on experience with Apache Spark for large-scale processing and distributed computing.\nPrior work integrating classical ML with Gen...","description_format":"text","description_chars":12612,"description_truncated":true,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Simulation & Digital Twin Software","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-10-11T03:56:46Z"}],"visa":[],"liveness":{"score":12,"band":"cold","label":"Long shot","p_open":1,"p_active":0.413,"p_room":0.28,"age_days":83,"expected_fill_days":17,"reasons":["conf:1","win:tail","crowd:brand"],"computed_at":"2026-10-11T19:29:12Z"},"pay":null,"html_url":"https://alion.io/job/keysight-machine-learning-engineer","json_url":"https://alion.io/job/keysight-machine-learning-engineer.json","meta":{"generated_at":"2026-10-11T19:29:12Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":6329,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-12T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":53596},"rest":"https://alion.io/mcp/rest/get_company?id=53596"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkeysight-machine-learning-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkeysight-machine-learning-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkeysight-machine-learning-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/keysight-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkeysight-machine-learning-engineer"}]}