{"id":3448649,"url":"https://alion.io/job/keysight-machine-learning-engineer-2","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":{"grade":"A","score":95,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":109,"computed_at":"2026-10-31T05:58:55Z"}},"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":56000,"max_usd":139000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"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","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-29T08:58:00Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T05:52:34Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"OverviewKeysight 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.\nAt 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-winningculture.Learn more about our benefits.\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.\nKey Responsibilities\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.\nKey Responsibilities\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 proce...","description_format":"text","description_chars":12689,"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":["EDA Software","Simulation & Digital Twin Software","Test & Measurement Instruments"],"lifecycle":[{"event":"open","at":"2026-10-29T09:54:54Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":1,"expected_fill_days":109,"reasons":["conf:0","velocity","win:early","comp:brand"],"computed_at":"2026-10-31T05:58:55Z"},"pay":null,"html_url":"https://alion.io/job/keysight-machine-learning-engineer-2","json_url":"https://alion.io/job/keysight-machine-learning-engineer-2.json","meta":{"generated_at":"2026-10-31T06:23:21Z","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":9411,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-01T00:00:00Z"}},"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-2"},{"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-2"},{"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-2"},{"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-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkeysight-machine-learning-engineer-2"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3448649}},{"tool":"search_jobs","arguments":{"company":"keysight.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3448649},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}