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 like Apache Spark, vector databases, and distributed processing patterns.
Stay 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.
Qualifications
Must-have qualifications
Master's degree in Machine Learning, Computer Science, Data Science, Statistics, Quantitative Mathematics, or a closely related field.
4+ 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.
Strong hands-on expertise in classical ML frameworks (Scikit-learn, XGBoost) and deep learning/NLP (TensorFlow/PyTorch, RNNs/LSTMs)
Practical 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).
Demonstrated success developing scalable summarization/information extraction pipelines for large document sets and production-grade anomaly detection/predictive models on numerical/time-series data.
Proficiency in production-grade Python, clean code practices, Git, testing, CI/CD, and MLOps best practices (model monitoring, drift detection, automated retraining).
Solid experience with AWS Bedrock (Knowledge Bases, custom models, Lambda/Step Functions for orchestration) or comparable GenAI platforms.
Familiarity with Agile/Scrum, sprint-based delivery, cross-functional collaboration, and rigorous QA/validation of ML/GenAI systems (evaluation metrics, bias/risk assessment).
Fluency in English, including technical terminology.
Strongly preferred
Domain exposure to manufacturing, semiconductors, sensor-based analytics, test/measurement instrumentation, or industrial risk analytics.
Hands-on experience with Apache Spark for large-scale processing and distributed computing.
Prior work integrating classical ML with GenAI (e.g., hybrid pipelines, using classical models for filtering/reranking in RAG).
A portfolio or demonstrable projects showing innovative, production-impactful solutions combining classical ML and Generative AI in real-world settings.
Experience 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).
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
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 like Apache Spark, vector databases, and distributed processing patterns.
Stay 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.
Must-have qualifications
Master's degree in Machine Learning, Computer Science, Data Science, Statistics, Quantitative Mathematics, or a closely related field.
4+ 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.
Strong hands-on expertise in classical ML frameworks (Scikit-learn, XGBoost) and deep learning/NLP (TensorFlow/PyTorch, RNNs/LSTMs)
Practical 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).
Demonstrated success developing scalable summarization/information extraction pipelines for large document sets and production-grade anomaly detection/predictive models on numerical/time-series data.
Proficiency in production-grade Python, clean code practices, Git, testing, CI/CD, and MLOps best practices (model monitoring, drift detection, automated retraining).
Solid experience with AWS Bedrock (Knowledge Bases, custom models, Lambda/Step Functions for orchestration) or comparable GenAI platforms.
Familiarity with Agile/Scrum, sprint-based delivery, cross-functional collaboration, and rigorous QA/validation of ML/GenAI systems (evaluation metrics, bias/risk assessment).
Fluency in English, including technical terminology.
Strongly preferred
Domain exposure to manufacturing, semiconductors, sensor-based analytics, test/measurement instrumentation, or industrial risk analytics.
Hands-on experience with Apache Spark for large-scale processing and distributed computing.
Prior work integrating classical ML with GenAI (e.g., hybrid pipelines, using classical models for filtering/reranking in RAG).
A portfolio or demonstrable projects showing innovative, production-impactful solutions combining classical ML and Generative AI in real-world settings.
Experience 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).
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
