685 Machine Learning jobs in Singapore

We're looking for an experienced Machine Learning Engineer to design, build, and deploy scalable ML solutions while ensuring compliance with AI governance and responsible AI practices. It covers the full ML lifecycle from data pipelines and ML Operations deployment to model monitoring and stakeholder collaboration in a global banking environment.

Client Details

Our client is a globally established financial institution with a major technology hub in Asia. They play a critical role in supporting international banking operations through the development of in-house applications and enterprise-level digital initiatives. With strong regional integration and a forward-looking tech strategy, they continue to enhance operational efficiency and drive innovation across global markets.

Description

Key Responsibilities:

• Partner with data scientists and business teams to define ML solutions and develop Proof of Concepts.
• Deploy and manage ML models in production using ML Operations best practices (CI/CD, monitoring, versioning).
• Build and maintain scalable data pipelines while ensuring model performance and reliability.
• Ensure compliance with AI governance, responsible AI practices, and audit requirements.
• Support data exploration, feature engineering, and model development when required.
• Automate retraining, testing, and monitoring to sustain long-term model accuracy.
• Document ML workflows and collaborate with DevOps, IT, and security teams for enterprise integration.

Profile

A successful candidate should have:

• Have Master's degree in AI/ML or data science with proven model design and development expertise.
• At least 6 years' experience in data science and ML, including at least 3 years in ML engineering roles.
• Hands-on experience across the full ML lifecycle: data preparation, model development, deployment, and monitoring.
• Strong Python programming skills and familiarity with ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Proficiency in NoSQL databases (Graph DB experience a plus) and use of ML Operations tools (MLflow, Airflow, Kubeflow, etc.).
• Experience with cloud platforms (AWS, GCP, Azure), CI/CD pipelines, and containerization (Docker, Kubernetes).
• Knowledge of responsible AI practices, governance, compliance, and techniques like NLP, time series, and supervised/unsupervised learning.
• Strong problem-solving, communication, and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders in diverse environments.

Job Offer

Why this role?

• Opportunity to work on cutting-edge AI/ML projects within a global banking environment
• Exposure to enterprise-scale ML Operations practices and full ML lifecycle implementation
• Career growth through continuous learning and professional development
• Competitive compensation and attractive benefits package
• Collaborative and multicultural workplace culture

To apply online please click the 'Apply' button. For a confidential discussion about this role please contact Cheryl Sim (Lic No: R / EA No.: 18C9065) on Michael Page International Pte Limited, company number N (including Page Executive A) and Page Personnel Recruitment Pte Ltd (Registration Number: C)) operates under the EA Licence Numbers of 18S9099 and 18C9065.

Responsibilities:

• Collaborate with data scientists and business stakeholders to define ML solutions and develop Proof of Concepts (PoCs).
• Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD).
• Build and maintain scalable data pipelines and monitor model performance.
• Ensure models comply with organizational AI policies, responsible AI practices, and audit requirements.
• Support data exploration, feature engineering, and occasional model building.
• Automate model retraining, testing, and monitoring to maintain long-term performance.
• Document ML workflows, governance checkpoints, and risk assessments.
• Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms.

Requirements:

• Masters degree in AI, ML, or Data Science.
• 6+ years in data science/ML, with 3+ years in ML engineering.
• Experience across the end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.
• Strong Python programming skills (pandas, scikit-learn, TensorFlow/PyTorch).
• Experience with NoSQL databases; Graph database experience desirable.
• Hands-on with MLOps tools (MLflow, TFX, Airflow, Kubeflow, etc.).
• Familiarity with cloud platforms (GCP, AWS, Azure) for ML deployment.
• Solid knowledge of data science techniques (supervised/unsupervised learning, NLP, time series).
• Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
• Understanding of AI governance, model risk management, and regulatory requirements.
• Ability to communicate technical concepts to non-technical stakeholders.

Preferred:

• Experience with Responsible AI frameworks and bias/fairness testing.
• Exposure to feature stores, model registries, and data versioning.
• Knowledge of data privacy, anonymization, and compliance in regulated industries (e.g., banking, healthcare).

To apply, please visit and search for Job Reference: 9396Y33Y

To learn more about this opportunity, please contact Yingying at

We regret that only shortlisted candidates will be notified.

GMP Technologies (S) Pte Ltd | EA Licence: 11C3793 | EA Personnel: Lai Yingying | Registration No: R

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By applying for this role, you consent to GMP Recruitment Services (S) Pte Ltds PDPA and e2is PDPA .

About Us

iCOMPASS Pte. Ltd. is a Singapore-headquartered RegTech company and spin-off from a leading advisory firm. iCOMPASS develops the world's first AI-powered compliance operating system designed for financial institutions. Designed in Singapore and built for global scalability, iCOMPASS is redefining how the financial industry approaches compliance in the age of AI.

Built at the intersection of regulatory expertise and cutting-edge technology, iCOMPASS transforms compliance from a reactive obligation into a proactive governance framework. With a vision to make regulatory compliance accessible, seamless, and adaptive, iCOMPASS empowers financial institutions of all sizes to reduce cost, mitigate risk, and enhance productivity.

Roles & Responsibilities

The successful candidate will design, build, and optimize LLM-driven solutions that support regulatory compliance, risk management. This role requires a deep understanding of both machine learning/NLP systems and compliance frameworks. The following summarises the scope of work:

• Model Development & Deployment

• Develop custom pipelines integrating structured (databases, transactions) and unstructured (news, documents, chat logs) data for compliance workflows.

• Implement explainability and interpretability layers to meet regulatory expectations.

• Compliance & Risk Applications

• Build systems that detect and classify financial crime risks (PEPs, RCAs, UBOs, sanctions exposure).

• Automate compliance document processing, regulatory reporting, and policy interpretation.

• Work closely with compliance officers to translate regulatory obligations into machine-executable rules.

• Systems Integration

• Deploy LLM-powered applications into enterprise-grade financial compliance platforms.

• Ensure robustness, scalability, and security of deployed models in production environments.

• Collaborate with engineering teams to integrate APIs, knowledge graphs, and third-party datasets.

• Research & Innovation

• Stay current with LLM advancements (RAG, fine-tuning, agents) and evaluate their applicability to compliance.

• Prototype tools for proactive risk detection and regulatory change monitoring.

• Others: Assist in ad-hoc projects or initiatives as assigned by management.

Requirements:

• Bachelor's Degree in Computer Science and any other relevant disciplines
• Strong analytical skills with the ability to assess risk and regulatory impact
• Ability to work independently and as part of a team in a fast-paced environment
• Excellent communication and presentation skills

Why Join Us?

• Engage in exciting projects to enhance compliance processes through innovation.
• Collaborative and dynamic work environment with opportunities for growth.
• We believe in rewarding impact and exceptional performance may be recognized with equity participation opportunities
• If you are looking to create impact in the regulated industry through technology transformation, this job is for you

If you are passionate about cross-pollinating regulatory compliance and technology, and enjoy problem-solving in a fast-paced environment, we invite you to apply for this role

Key Responsibilities

• Model Selection & Optimization: Identify, experiment with, and fine-tune state-of-the-art machine learning models for tasks such as video analytics, object recognition/detection, fraud detection, and multimodal generative AI.
• Deep Learning & LLMs: Work with transformer architectures, foundation models, and generative AI to develop and enhance multimodal AI solutions.
• Computer Vision: Develop and optimize models for real-time object detection, tracking, and recognition in video streams, ensuring performance in diverse conditions.
• Fraud Detection and other anomaly detection: Design and implement machine learning models for anomaly detection and fraud prevention using advanced statistical and AI techniques.
• Data Engineering & Processing: Preprocess large datasets, design efficient pipelines for real-time and batch processing, and integrate multimodal data sources (images, text, audio, video).
• Deployment & Scalability: Deploy models in production using cloud-based or edge computing solutions, ensuring performance, scalability, and cost-efficiency.
• Research & Innovation: Stay updated on the latest AI research, evaluate emerging models, and propose enhancements to improve performance and robustness.
• Collaboration & Integration: Work closely with software engineers, data scientists, and domain experts to integrate AI models into end-user applications.

Requirements

• Degree in Computer Science, AI, Machine Learning, or a related field.
• 4 to 5 years of relevant experience
• Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Proficiency in computer vision techniques, including object detection (YOLO, Faster R-CNN), video analytics, and multimodal learning.
• Hands-on experience with LLMs, transformers (GPT, BERT, T5, CLIP, etc.), and multimodal AI for text, image, and video synthesis.
• Experience in fraud detection and/or anomaly detection using machine learning, pattern recognition, and risk modeling.
• Experience in AWS SageMaker for model training and deployment preferred.
• Familiarity with MLOps and deployment on cloud platforms (AWS and/or GCP preferred) or edge devices.
• Strong problem-solving and analytical thinking abilities.
• Ability to work in a fast-paced, research-driven environment and adapt to evolving challenges.
• Excellent communication skills for presenting findings and collaborating across teams.
• Experience working with autonomous systems, robotics, or edge AI is a plus.

Interested parties, please click the "Apply Now" below AND apply via the GO portal.

We regret that only shortlisted applicants would be notified.

Toh Wen Qi, Celeste | REG No : R

PERSOLKELLY SINGAPORE PTE LTD | EA License No : 01C4394

By sending us your personal data and curriculum vitae (CV), you are deemed to consent to PERSOLKELLY Singapore Pte Ltd and its affiliates to collect, use and disclose your personal data for the purposes set out in the Privacy Policy available at You acknowledge that you have read, understood, and agree with the Privacy Policy.

Location: Singapore (WFO, 5 Days)

Company: Confidential

Payroll Company: IIT Matrix

Duration: 1-year initial contract with high potential for long-term extensions

Job Title: Machine Learning Engineer (NLP/Generative AI)

We are looking for an innovative and production-focused engineer who thrives in a collaborative environment and is passionate about turning groundbreaking prototypes into scalable, high-performing systems.

• Algorithm Development & Prototyping: Collaborate with a specialized team of machine learning engineers to prototype, develop, and ship world-class algorithms that advance the state of the art in conversational AI.
• LM & Generative AI Exploration: Lead the exploration and application of Large Language Models (e.g., GPT) and Generative AI, venturing into novel areas to solve complex problems and enhance system capabilities.
• End-to-End Model Pipeline: Own the full lifecycle of model development—from ideation and data analysis to building, fine-tuning, and deploying models into production. Design and implement robust automation pipelines.
• Technical Execution: Make critical decisions on technology selection, balancing the use of out-of-the-box solutions against building custom models to optimally meet project goals.
• Cross-Functional Collaboration: Partner closely with software engineers to integrate ML models into production environments, ensuring high performance, scalability, and reliability.
• Data Analysis & Optimization: Perform ongoing data analysis to inform model development, fine-tune existing systems, and continuously optimize results.
• Knowledge Leadership: Maintain deep expertise in the latest advancements in AI/ML technology and contribute to the team's knowledge base.
• Communication: Clearly communicate complex technical concepts, analysis results, and project updates to business partners and executives.

• A minimum of 3 years of professional experience in Machine Learning, with a specialized focus on Natural Language Processing (NLP).
• Hands-on experience with core NLP tasks: Text classification, named entity recognition, and text embeddings.
• Large Language Models (LLMs): Practical understanding of how to integrate and fine-tune LLMs within conversational AI systems.
• Strong programming skills: High proficiency in Python for data science and ML development.
• API & Microservices Development: Experience with FastAPI or similar frameworks for building and deploying model endpoints.
• Cloud Platform Experience: Proven ability to deploy, manage, and monitor machine learning models on AWS, GCP, or similar cloud infrastructure.

• Experience with Machine Translation systems and their application in building multilingual chatbot experiences.
• Familiarity with advanced techniques like Retrieval-Augmented Generation (RAG) for improving the accuracy and relevance of chatbot responses.
• A track record of innovation, demonstrated through publications, patents, or significant contributions to open-source projects.

Note: Looking for an Immediate joiners or short notice period candidates.

Direct message the job poster from SCIENTE

We are seeking an experienced Machine Learning / AI Engineer to join a dynamic and multicultural team within a leading global financial institution at the forefront of digital transformation and innovation. This role involves designing, building, and deploying end-to-end machine learning solutions across international and cross‑functional projects supporting strategic business functions across banking, finance, and risk domains. The ideal candidate will be hands‑on in both model development and engineering scalable, production‑grade ML systems, while ensuring alignment with AI governance and compliance standards. This is a 12 months contract hiring.

• Master’s degree in AI, Machine Learning, or Data Science;
• Must have 6+ years in data science and ML, including 3+ years in ML engineering lifecycle;
• Proficiency in Python and ML libraries (pandas, scikit‑learn, TensorFlow/PyTorch);
• Must have experience with NoSQL databases; exposure to graph databases is a plus;
• Familiarity with MLOps tools such has MLflow, TFX, Airflow, Kubeflow;
• Cloud deployment experience such as AWS, GCP, or Azure;
• Strong grasp of supervised/unsupervised learning, NLP, time series analysis;
• CI/CD pipeline and containerization expertise (Docker, Kubernetes);
• Understanding of AI governance, model risk, and regulatory compliance and the ability to explain technical concepts to non‑technical stakeholders;

• Knowledge of Responsible AI frameworks and fairness/bias testing;
• Experience with feature stores, model registries, and data versioning;
• Familiarity with data privacy, anonymization, and compliance in regulated sectors.

• Collaborate with data scientists and business teams to define ML solutions and build PoCs;
• Deploy and maintain ML models using MLOps best practices;
• Build scalable data pipelines and monitor model performance;
• Ensure models comply with internal AI policies and audit standards;
• Support feature engineering and occasional model development;
• Automate model retraining, testing, and performance tracking;
• Document workflows, governance checkpoints, and risk assessments;
• Work closely with DevOps, IT, and security teams to integrate ML solutions into enterprise platforms.

Should you be interested in this career opportunity, please send in your updated resume to at the earliest.

When you apply, you voluntarily consent to the disclosure, collection and use of your personal data for employment/recruitment and related purposes in accordance with the SCIENTE Group Privacy Policy, a copy of which is published at SCIENTE’s website (

Confidentiality is assured, and only shortlisted candidates will be notified for interviews.

EA Licence No. 07C5639

Overview

We are seeking a highly skilled and experienced Machine Learning / AI Engineer to join our dynamic and multicultural environment. The ideal candidate will have a strong foundation in data science, applied machine learning, and MLOps, with the ability to design, build, and deploy end-to-end ML solutions. This role combines technical expertise with cross-functional collaboration to deliver scalable and responsible AI systems aligned with organizational standards and compliance requirements.

• Collaborate with data scientists and business stakeholders to define ML solutions and develop Proof of Concepts (POCs).
• Design, build, and deploy production-grade machine learning models using MLOps best practices (versioning, CI/CD, monitoring, retraining).
• Build and maintain scalable data pipelines to support model performance and system efficiency.
• Ensure all models adhere to organizational AI governance, compliance, and ethical AI practices .
• Support data exploration , feature engineering , and model optimization as needed.
• Automate processes for model retraining, validation, and monitoring to maintain performance over time.
• Document ML workflows, governance checkpoints, and model risk assessments.
• Collaborate with DevOps, IT, and security teams to integrate ML systems into enterprise infrastructure.
• Communicate technical solutions and results clearly to both technical and non-technical audiences.
• Work independently while maintaining strong alignment with key project stakeholders.

Mandatory:

• Master's degree in Artificial Intelligence, Machine Learning, Data Science , or a related field.
• Minimum 6+ years of experience in data science and machine learning, including at least 3+ years in ML engineering roles.
• Proven experience in end-to-end ML lifecycle - from data preparation and model development to deployment and monitoring.
• Strong programming skills in Python (pandas, scikit-learn, TensorFlow, PyTorch, etc.).
• Hands-on experience with MLOps tools (MLflow, Airflow, TFX, Kubeflow, etc.).
• Familiarity with cloud platforms (AWS, GCP, Azure) for ML deployment.
• Strong understanding of NoSQL databases ; exposure to graph databases is advantageous.
• Experience with CI/CD pipelines and containerization tools (Docker, Kubernetes).
• Solid understanding of AI governance , model risk management , and compliance principles.
• Excellent communication skills with the ability to explain technical concepts to diverse audiences.

• Experience implementing Responsible AI frameworks and performing bias/fairness assessments .
• Familiarity with feature stores , model registries , and data versioning .
• Understanding of data privacy , anonymization , and compliance best practices.

• Strong analytical and problem-solving abilities.
• Excellent organizational and interpersonal communication skills.
• Eagerness to learn and adopt emerging technologies.
• Familiarity with software development life cycle and Agile methodologies.
• Collaborative mindset with respect for cultural diversity and global teamwork.

If interested, you can click on "Apply here" or write an e-mail to ***@adecco.com with your updated resume.

Note: Only shortlisted candidates will be contacted back.

Dimple Jain
Direct Line: 8110 4***
EA License No: 91C2918
Personnel Registration Number: R

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In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences.

• Build machine learning solutions to respond to and mitigate business risks in eHealthcare products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc.
• Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups.
• Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis.

• Master or above degree in CS, EE or other relevant, machine-learning-heavy majors.
• Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm.
• Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning.
• Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.