Machine Learning Engineer

KEYSIGHT TECHNOLOGIES SINGAPORE (SALES) PTE. LTD.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Keysight Technologies Singapore (Sales) Pte. Ltd. invites an experienced AI/ML Engineer to design, develop, and scale advanced AI/ML solutions across our analytics platform for manufacturing and semiconductors.

You will combine classical ML with Generative AI, building production-grade systems for anomaly detection, predictive maintenance, and market intelligence. You will own end-to-end AI/ML initiatives from data modeling to unstructured text processing, in a regulated industrial setting,

Qualifications

• Master's degree in Machine Learning, Computer Science, Data Science, Statistics, or related field.
• 4+ years of professional experience as a Machine Learning/AI Engineer with end-to-end deployment.
• Strong hands-on expertise in Scikit-learn, XGBoost, and deep learning/NLP (TensorFlow/PyTorch, RNNs/LSTMs).
• Experience building RAG architectures, prompt engineering, vector DB optimization, and agentic workflows.
• Proven ability to develop scalable summarization and information extraction pipelines for large data sets.
• Proficiency in production-grade Python, Git, CI/CD, and MLOps (monitoring, drift, retraining).
• Solid experience with AWS Bedrock or similar GenAI platforms.
• Familiarity with Agile/Scrum, cross-functional collaboration, and rigorous ML validation.
• Fluency in English with strong technical vocabulary.

Responsibilities

• Lead architecture and continuous improvement of unified AI/ML capabilities integrating classical ML with GenAI platforms.
• Design and implement anomaly detection and predictive maintenance on real-time sensor data with drift monitoring.
• Build and scale RAG pipelines and agentic workflows for automated test plan generation from historical data.
• Develop intelligent summarization and information extraction pipelines from thousands of articles into actionable reports.
• Own a customer-facing GenAI Q&A chatbot delivering domain-specific semiconductor insights.
• Tackle ML problems (regression/classification/clustering/time-series) and integrate with GenAI components.
• Apply NLP techniques to extract insights from unstructured sources and ensure production reliability.
• Collaborate in Agile/Scrum with MLOps, data engineering, domain experts, and product teams.
• Perform model evaluation, hyperparameter tuning, feature engineering, bias/risk assessment, and validation.
• Contribute to large-scale data pipelines using Spark, vector databases, and distributed processing.

Skills

Python

Machine Learning

NLP

RAG

LangChain/LangGraph

AWS Bedrock

TensorFlow/PyTorch

Git & CI/CD

MLOps

English proficiency

Education

Master's degree in ML/CS/Data Science

Tools

Apache Spark

Vector databases

Embeddings/hybrid search

Job description

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.

• 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).

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