Senior Machine Learning Engineer – Generative AI Platform

KEYSIGHT TECHNOLOGIES SINGAPORE (SALES) PTE. LTD.; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A leading technology firm in Singapore is seeking a Senior Machine Learning Engineer to develop and enhance their Generative AI platform. The role involves collaboration in building production-grade solutions, focusing on accuracy and reliability in regulated industrial environments. Candidates should have a Master’s degree and 1-3 years of experience in machine learning and Generative AI, along with strong programming and Agile methodology skills. This position offers a chance to impact intelligent manufacturing analytics and product excellence.

Benefits

Innovation-driven environment

Collaborative team

Qualifications

• 1-3 years of professional experience in ML/GenAI.
• Hands-on expertise in RAG architectures.
• Solid software engineering practices including CI/CD.

Responsibilities

• Lead the architecture and development of Generative AI platform.
• Design robust RAG pipelines for precise applications.
• Collaborate with team for end-to-end reliability in production.

Skills

Generative AI

Machine Learning

Python

Agile methodologies

Education

Master's degree in Machine Learning or related field

Tools

AWS Bedrock

Apache Spark

Job description

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to play a pivotal role in designing, developing, and scaling our company's unified Generative AI platform. This strategic initiative powers mission-critical applications in the manufacturing and semiconductor industries, where precision, reliability, and risk mitigation are non-negotiable.

You will collaborate closely with MLOps engineers, data scientists, product teams, and domain experts to build production-grade GenAI solutions that leverage large language models (LLMs), advanced retrieval-augmented generation (RAG), and emerging agentic architectures. Our platform drives tangible business value through automated test plan generation, market intelligence summarization, and expert-level customer support for our flagship analytic product.

This is a high-impact, hands-on position for someone who thrives on solving complex, domain-specific challenges in regulated industrial environments.

• Lead the architecture, development, and continuous enhancement of the company's core unified Generative AI platform, built primarily on AWS Bedrock.
• Design and implement robust RAG pipelines for high-precision applications, with a strong emphasis on accuracy, hallucination mitigation, and risk minimization — particularly in the generation of manufacturing test plans from vast historical datasets of test plans and measurement instrument data.
• Experience in model lifecycle monitoring using any Cloud tools, to detect concept and data drift of existing model deployed.
• Develop intelligent workflows to ingest, process, and distill thousands of scraped news articles, press releases, and open-source intelligence into concise, actionable market intelligence summaries (typically reducing input to 12–48 highly relevant documents), utilizing advanced techniques such as intelligent chunking, semantic relevance filtering (e.g., embeddings + k‑NN), map‑reduce summarization patterns, TF‑IDF augmentation, or AI agentic orchestration when superior to traditional methods.
• Build and maintain a customer-facing Q&A chatbot for our proprietary products, enabling users to query and gain deep insights into semiconductor manufacturing risk identification based on sensor measurements and test plan data.
• Collaborate with MLOps engineers to ensure full end-to-end reliability, observability, versioning, automated testing, and CI/CD for all GenAI components in production.
• Contribute to prompt engineering, knowledge base curation, vector database optimization (e.g., embeddings tuning, hybrid search), Lambda function development, and Bedrock custom model/ Agent workflows.
• Partner cross-functionally in an Agile environment, participating actively in sprint planning, backlog refinement, technical design reviews, and iterative delivery to meet demanding timelines and quality standards.
• Stay abreast of the latest advancements in GenAI, RAG, agentic systems, and responsible AI practices, and proactively propose innovations that enhance platform capabilities and business outcomes.

Must-have qualifications

• Master's degree in Machine Learning, Computer Science, Quantitative Mathematics, Statistics, or a closely related field.
• 1–3 years of professional experience as a Machine Learning Engineer/ Data Scientist, with proven track record of independently owning end-to-end development, training, validation, and production deployment of ML/ GenAI models.
• Hands‑on expertise in Generative AI and RAG architectures, including practical experience with AWS Bedrock, Knowledge Bases, custom model fine‑tuning, prompt engineering, and Lambda function and Step functions for orchestration of GenAI workloads.
• Demonstrated experience building scalable summarization or information extraction pipelines handling very large document sets (thousands of articles), using map‑reduce, agentic patterns, or advanced filtering techniques.
• Solid software engineering foundation: production‑grade Python, clean code practices, testing, version control (Git), and CI/CD.
• Familiarity with Agile/ Scrum methodologies and experience delivering iteratively in sprint‑based environments.
• Strong quality mindset with background in QA/ validation of ML systems, including rigorous evaluation metrics, bias/ risk assessment, and reliability engineering.

Strongly preferred

• Fluency in English and all of its technical terms.
• Prior domain exposure to manufacturing, semiconductor processes, measurement/ test instrumentation, or risk analytics.
• Hands‑on experience with agentic AI frameworks, multi‑agent systems, and tools such as LangChain/ LangGraph, CrewAI, or Bedrock Agents.
• Practical knowledge of Apache Spark for large‑scale data processing, as well as associated databases and distributed computing patterns.
• Experience with MLOps best practices, model monitoring, drift detection, and automated retraining pipelines.

If you are a proactive, detail-oriented engineer passionate about delivering precise, high-stakes Generative AI solutions in industrial contexts, we invite you to bring your expertise to our team and help shape the future of intelligent manufacturing analytics.

We offer a collaborative, innovation-driven environment where your contributions will have direct, visible impact on product excellence and customer success.