1,255 Machine Learning Engineer jobs in Singapore

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

Be careful - Don’t provide your bank or credit card details when applying for jobs. Don't transfer any money or complete suspicious online surveys. If you see something suspicious, report this job ad.

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.

Job Summary

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 understand use cases and define suitable ML solutions.
• Design, engineer, and deploy machine learning models into production using MLOps best practices (model versioning, CI/CD, and monitoring).
• Build and maintain data pipelines and ensure scalability and maintainability of deployed models.
• Support data exploration, feature engineering, and model development when required.
• Automate model retraining, testing, and monitoring processes to ensure consistent performance over time.
• Ensure all ML models comply with Responsible AI standards , governance, and audit requirements.
• Document ML workflows, governance checkpoints, and risk assessments.
• Partner with DevOps, IT, and security teams to integrate ML solutions into enterprise systems.
• Maintain close communication with key stakeholders while demonstrating autonomy and accountability.

Mandatory:

• Master’s degree in Artificial Intelligence, Machine Learning, Data Science, or a related field .
• 6+ years of experience in data science and machine learning, with at least 3+ years in ML engineering roles.
• Proven expertise in the end-to-end ML lifecycle — from data preprocessing to deployment and model monitoring.
• Strong programming skills in Python and familiarity with key ML libraries (pandas, scikit-learn, TensorFlow, PyTorch, etc.).
• Experience with NoSQL databases (graph database experience is a plus).
• Proficiency in MLOps tools such as MLflow, TFX, Airflow, or Kubeflow.
• Hands-on experience with cloud platforms like AWS, Azure, or GCP for ML deployment.
• Solid understanding of CI/CD pipelines , Docker , and Kubernetes .
• Strong grasp of AI governance, model risk management , and regulatory compliance for AI solutions.
• Excellent communication skills with the ability to present technical information to non-technical stakeholders.

Preferred:

• Experience with Responsible AI frameworks and bias/fairness assessment.
• Knowledge of feature stores , model registries , and data versioning tools .
• Understanding of data privacy, anonymization , and compliance in regulated environments.

• Strong analytical, problem-solving, and organizational skills.
• Ability and willingness to learn and adopt emerging technologies .
• Excellent collaboration and communication skills in a multicultural environment.
• Awareness of software development methodologies and ability to follow defined processes.
• Respect for cultural diversity and a commitment to inclusive teamwork.

Next Step:

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

NOTE: - Only shortlisted candidates will be contacted back.

Dimple Jain

We’re looking for a Machine Learning Engineer to join our dynamic AI/ML team. You’ll build and enhance the intelligent systems and models that power our products. You’ll work closely with data scientists, data engineers, and product teams to translate research prototypes into AI‑powered solutions that drive business value.

• Build ML Models: Design, develop, and validate machine learning models for problems such as AI‑driven predictive maintenance, operational optimization, and AI agents.
• Improve Models: Fine‑tune models to make them more accurate and perform better.
• Work with Data: Collaborate with data engineers to get the right data ready for models and understand it well.
• Deploy Models: Help get models ready and put them into action so they can be used by our products or services.
• Monitor Performance: Keep an eye on how models are doing and fix any issues that come up.
• Research & Learn: Stay up‑to‑date with new ML techniques and find ways to apply them to our challenges.
• Collaborate: Work closely with other engineers, data scientists, and product managers.

• Experience: 5+ years of experience building and deploying machine learning models.
• Coding: Strong skills in Python and familiar with ML libraries like PyTorch, TensorFlow, or scikit‑learn.
• ML Basics: Good understanding of common machine learning algorithms (e.g., regression, classification, clustering).
• Data Skills: Able to work with data, including cleaning and preparing it for models.
• Problem Solver: Great at breaking down complex problems and finding solutions using ML.
• Team Player: Good communication skills and enjoy working with a team.

Direct message the job poster from Trinity Consulting Services ("TRINITY")

• 6+ years of experience in data science and machine learning, with at least 3+ years in ML engineering roles.
• Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.
• Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch, etc.).
• Strong knowledge in NoSQL databases (any experience in Graph database is desirable)
• Experience with MLOps tools: MLflow, TFX, Airflow, Kubeflow, or similar.
• Familiarity with cloud platforms (GCP, AWS, or Azure) for ML deployment.
• Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.
• Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
• Strong understanding of AI governance, model risk management, and regulatory requirements in AI.
• Ability to communicate technical concepts to non-technical stakeholders.

• Mid-Senior level

• Full-time

• Information Technology
• Industries: IT Services and IT Consulting

Referrals increase your chances of interviewing at Trinity Consulting Services ("TRINITY") by 2x

Get notified about new Machine Learning Engineer jobs in Singapore, Singapore .

Queenstown, Central Singapore Community Development Council, Singapore 8 months ago

We’re looking for a Machine Learning Engineer to join our dynamic AI/ML team. You’ll build and enhance the intelligent systems and models that power our products. You’ll work closely with data scientists, data engineers, and product teams to translate research prototypes into AI‑powered solutions that drive business value.

• Build ML Models: Design, develop, and validate machine learning models for problems such as AI‑driven predictive maintenance, operational optimization, and AI agents.
• Improve Models: Fine‑tune models to make them more accurate and perform better.
• Work with Data: Collaborate with data engineers to get the right data ready for models and understand it well.
• Deploy Models: Help get models ready and put them into action so they can be used by our products or services.
• Monitor Performance: Keep an eye on how models are doing and fix any issues that come up.
• Research & Learn: Stay up‑to‑date with new ML techniques and find ways to apply them to our challenges.
• Collaborate: Work closely with other engineers, data scientists, and product managers.

• Experience: 5+ years of experience building and deploying machine learning models.
• Coding: Strong skills in Python and familiar with ML libraries like PyTorch, TensorFlow, or scikit‑learn.
• ML Basics: Good understanding of common machine learning algorithms (e.g., regression, classification, clustering).
• Data Skills: Able to work with data, including cleaning and preparing it for models.
• Problem Solver: Great at breaking down complex problems and finding solutions using ML.
• Team Player: Good communication skills and enjoy working with a team.