1,372 AI Powered Systems jobs in Singapore

Crédit Agricole Corporate and Investment Banking (Crédit Agricole CIB) is the corporate and investment banking arm of Crédit Agricole Group, world’s 10th largest bank by total assets.

Our Singapore center ("ISAP" or "Information Systems Asia Pacific") is the 2nd largest IT setup (after Paris Head Office) for Crédit Agricole CIB's worldwide business. We work daily with international branches located in 30 markets by:

• Envisioning and preparing the Bank’s futures information systems
• Partnering and supporting core banking flagships and transverse areas in their large scale development projects
• Providing premium In-house Banking applications

This unique positioning empowers us to bring our core banking business a sustainable competitive advantage on the market.

We seek innovative and agile people sharing our mindset to support ambitious and forthcoming technological challenges.

Position

In a challenging and multicultural environment, we are looking for a Machine Learning /AI Engineer to join our Digital Excellence Centre (DEC) department of Crédit Agricole CIB. The department handles the development of transversal and international projects.

We are looking for a seasoned Machine Learning Engineer with a strong background in data science and applied ML , who can design, build, and deploy end-to-end machine learning solutions. This hybrid role requires a hands-on expert who can not only build models when needed but also engineer scalable, production-grade ML systems while adhering to the organization's AI governance and compliance standards.

Main responsibilities

• Collaborate with data scientists and business stakeholders to understand use cases and define ML solution; work on Proof of Concepts wherever needed
• Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD, etc.).
• Build & maintain data pipelines and model performance for scalability and maintainability.
• Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements.
• Support data exploration, feature engineering, and occasional model building where needed.
• Automate model retraining, testing, and monitoring to ensure performance over time.
• Document ML workflows, governance checkpoints, and risk assessments.
• Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms.

The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders.

Qualifications and Profile

• 5 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.

Good to have:

• 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).

Mid-Senior level

Full-time

Information Technology

Banking, Financial Services, and Technology, Information and Media

Crédit Agricole Corporate and Investment Banking (Crédit Agricole CIB) is the corporate and investment banking arm of Crédit Agricole Group, world’s 10th largest bank by total assets.
Our Singapore center ("ISAP" or "Information Systems Asia Pacific") is the 2nd largest IT setup (after Paris Head Office) for Crédit Agricole CIB's worldwide business. We work daily with international branches located in 30 markets by:
Envisioning and preparing the Bank’s futures information systems
Partnering and supporting core banking flagships and transverse areas in their large scale development projects
Providing premium In-house Banking applications
This unique positioning empowers us to bring our core banking business a sustainable competitive advantage on the market.
We seek innovative and agile people sharing our mindset to support ambitious and forthcoming technological challenges.
Position
In a challenging and multicultural environment, we are looking for a
Machine Learning /AI Engineer
to join our Digital Excellence Centre (DEC) department of Crédit Agricole CIB. The department handles the development of transversal and international projects.
We are looking for a seasoned
Machine Learning Engineer
with a strong background in
data science and applied ML , who can design, build, and deploy end-to-end machine learning solutions. This hybrid role requires a hands-on expert who can not only build models when needed but also
engineer scalable, production-grade ML systems
while adhering to the organization's AI governance and compliance standards.
Main responsibilities
Collaborate with data scientists and business stakeholders to understand use cases and define ML solution; work on Proof of Concepts wherever needed
Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD, etc.).
Build & maintain data pipelines and model performance for scalability and maintainability.
Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements.
Support data exploration, feature engineering, and occasional model building where needed.
Automate model retraining, testing, and monitoring to ensure performance over time.
Document ML workflows, governance checkpoints, and risk assessments.
Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms.
The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders.
Qualifications and Profile
5 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.
Good to have:
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).
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Information Technology
Industries
Banking, Financial Services, and Technology, Information and Media
#J-18808-Ljbffr

Overview

AI/ML Engineer responsible for architecting, building, and maintaining end-to-end AI/ML pipelines and integrating AI models into enterprise systems while ensuring governance, ethics, and regulatory compliance.

• Architect, build, and maintain end-to-end AI/ML pipelines (data ingestion, preprocessing, training, deployment, predictive analytics / monitoring).
• Collaborate with data scientists and domain experts to operationalize experimental models, optimizing for performance, scalability, and latency to ensure high-quality datasets.
• Implement and advocate for AI/MLOps practices (CI/CD for ML, model versioning, feature stores) using modern tools.
• Optimize model inference for production environments (e.g., using TensorRT, ONNX, pruning, quantization).
• Write robust, testable, and maintainable code in a collaborative setting using GitHub.
• Integrate AI models into enterprise systems using APIs and cloud-native services (AWS, Azure).
• Ensure models meet business objectives while adhering to ethical AI and governance frameworks.
• Provide technical support and training to users.
• Troubleshoot and resolve system issues.
• Participate in business analysis and technical design sessions.
• Ensure compliance with industry regulations.
• Document system processes and present progress reports.
• Collaborate with team members and other departments to ensure smooth operation.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field. A PhD is a plus.
• Minimum of 5-7 years of professional experience in AI/ML model development, AI/MLOps, and cloud-based deployment, with at least 3-4 years focused on building and deploying machine learning models and AI solution in a production environment.
• Proven track record of taking AI/ML projects from concept to deployment and monitoring.
• Experience working in regulated industries maritime in Singapore is advantageous.
• Excellent analytical and problem-solving abilities.
• Strong communication and interpersonal skills.
• Ability to manage multiple projects and deliverables simultaneously.

Seatrium Pioneer Yard, 50 Gul Road Singapore
(Island wide transport provided)
Mon - Thu: 8am - 5:15pm, Fri: 8am to 4:30pm

Interested candidates are invited to send an updated resume with current and expected salary and earliest availability. We regret that only shortlisted candidates will be notified.

Please note that your personal data disclosed to Seatrium Limited and our group of companies shall be used for evaluation and processing in accordance with our recruitment processes and policies. By providing your personal data, you have consented to the aforesaid purpose under the Personal Data Protection Act 2012.

• Engineering and Information Technology
• Shipbuilding

Referrals increase your chances of interviewing at Seatrium by 2x

Sign in to set job alerts for “Artificial Intelligence Engineer” roles. Queenstown, Central Singapore Community Development Council, Singapore

Overview
AI/ML Engineer responsible for architecting, building, and maintaining end-to-end AI/ML pipelines and integrating AI models into enterprise systems while ensuring governance, ethics, and regulatory compliance.
Responsibilities
Architect, build, and maintain end-to-end AI/ML pipelines (data ingestion, preprocessing, training, deployment, predictive analytics / monitoring).
Collaborate with data scientists and domain experts to operationalize experimental models, optimizing for performance, scalability, and latency to ensure high-quality datasets.
Implement and advocate for AI/MLOps practices (CI/CD for ML, model versioning, feature stores) using modern tools.
Optimize model inference for production environments (e.g., using TensorRT, ONNX, pruning, quantization).
Write robust, testable, and maintainable code in a collaborative setting using GitHub.
Integrate AI models into enterprise systems using APIs and cloud-native services (AWS, Azure).
Ensure models meet business objectives while adhering to ethical AI and governance frameworks.
Provide technical support and training to users.
Troubleshoot and resolve system issues.
Participate in business analysis and technical design sessions.
Ensure compliance with industry regulations.
Document system processes and present progress reports.
Collaborate with team members and other departments to ensure smooth operation.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field. A PhD is a plus.
Minimum of 5-7 years of professional experience in AI/ML model development, AI/MLOps, and cloud-based deployment, with at least 3-4 years focused on building and deploying machine learning models and AI solution in a production environment.
Proven track record of taking AI/ML projects from concept to deployment and monitoring.
Experience working in regulated industries maritime in Singapore is advantageous.
Excellent analytical and problem-solving abilities.
Strong communication and interpersonal skills.
Ability to manage multiple projects and deliverables simultaneously.
Our Address And Working Hours
Seatrium Pioneer Yard, 50 Gul Road Singapore
(Island wide transport provided)
Mon - Thu: 8am - 5:15pm, Fri: 8am to 4:30pm
Interested candidates are invited to send an updated resume with current and expected salary and earliest availability. We regret that only shortlisted candidates will be notified.
Please note that your personal data disclosed to Seatrium Limited and our group of companies shall be used for evaluation and processing in accordance with our recruitment processes and policies. By providing your personal data, you have consented to the aforesaid purpose under the Personal Data Protection Act 2012.
Job Function and Industry
Engineering and Information Technology
Shipbuilding
Referrals increase your chances of interviewing at Seatrium by 2x
Sign in to set job alerts for “Artificial Intelligence Engineer” roles. Queenstown, Central Singapore Community Development Council, Singapore
#J-18808-Ljbffr

Responsibilities

• Collaborate with data scientists and business stakeholders to define ML solutions and develop 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.

• 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.

• 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 GMPRecruit.com 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

This is in partnership with the Employment and Employability Institute Pte Ltd (e2i).

e2i is the empowering network for workers and employers seeking employment and employability solutions. e2i serves as a bridge between workers and employers, connecting with workers to offer job security through job-matching, career guidance and skills upgrading services, and partnering employers to address their manpower needs through recruitment, training, and job redesign solutions. e2i is a tripartite initiative of the National Trades Union Congress set up to support nation-wide manpower and skills upgrading initiatives.

Responsibilities
Collaborate with data scientists and business stakeholders to define ML solutions and develop 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 GMPRecruit.com 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
This is in partnership with the Employment and Employability Institute Pte Ltd (e2i).
e2i is the empowering network for workers and employers seeking employment and employability solutions. e2i serves as a bridge between workers and employers, connecting with workers to offer job security through job-matching, career guidance and skills upgrading services, and partnering employers to address their manpower needs through recruitment, training, and job redesign solutions. e2i is a tripartite initiative of the National Trades Union Congress set up to support nation-wide manpower and skills upgrading initiatives.
#J-18808-Ljbffr

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

This is in partnership with the Employment and Employability Institute Pte Ltd (e2i).

e2i is the empowering network for workers and employers seeking employment and employability solutions. e2i serves as a bridge between workers and employers, connecting with workers to offer job security through job-matching, career guidance and skills upgrading services, and partnering employers to address their manpower needs through recruitment, training, and job redesign solutions. e2i is a tripartite initiative of the National Trades Union Congress set up to support nation-wide manpower and skills upgrading initiatives.

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

Company Summary :

ELGO AI is a fast-growing startup redefining how enterprises build and deploy AI. We offer a powerful no-code enterprise-grade GenAI platform that enables organizations to create custom LLM-powered applications such as chatbots, smart document workflows, and automation agents 10x faster than traditional methods. Abstracting away the complexities of a production GenAI system, we aim to simplify enterprise AI adoption by making advanced GenAI capabilities accessible to non-technical teams. We are passionate about workflow orchestration, context-aware agents, full transparency and observability layer, scalable designs and enterprise-grade deployment, all grounded in responsible, secure, and user-centric design. Our vision is to become the go-to operating platform for practical AI solutions in Asia Pacific.

Job Description:

As an AI Engineer, you'll play a key role in the development and optimization of ELGO's no-code platform and LLM-based workflows. Instead of model training or fine-tuning, your focus will be on building reusable, configurable, and scalable LLM-powered components and highly optimized task agents that can be orchestrated by users without writing code. We also constantly push the frontiers on LLM techniques and designs to ensure that our agents are state-of-the-art.

You'll work closely with our CTO and engineering team to implement advanced LLM workflows, multi-agent logic, evaluation mechanisms, and performance monitoring that power real-world business applications.

The ideal person for the role would love the fast-changing and dynamic environment, working with new technologies and translating that into real world impact. He/she would also enjoy the opportunity to work on multiple real-world LLM systems, and constantly push him/herself to be better. He/she is would also be extremely proactive, both in working and learning.

Caution: this is likely going to be the toughest role ever you would have in your life, but it could also be the most rewarding.

Roles & Responsibilities:

• Develop and optimize backend systems for LLM workflow orchestration (retrieval, chaining, evaluation, memory, task-based agent optimizations).

• Implement reusable components (e.g., retrieval nodes, validation agents, evaluators) to power the no-code platform.

• Integrate and manage third-party models (e.g., OpenAI, Anthropic, AWS Bedrock, open-source models).

• Develop and maintain multi-agent and tool-augmented AI workflows for common enterprise use cases.

• Work on document processing, cross-system integrations, RAG pipelines, contextual memory management, and prompt engineering best practices.

• Research and development new techniques to tackle the toughest problems in LLM applications.

• Collaborate with front-end and platform engineers to support the no-code interface.

• Conduct systematic evaluations and performance tracking of LLM applications in production.

• Stay updated with emerging GenAI and agentic framework trends.

Requirements:

Core Requirements

• Bachelor's degree in Computer Science, AI/ML Engineering, or equivalent experience with about 2-4 years relevant working experience. Candidates without formal education but with strong practical experience are welcomed.

• Strong Python programming skills with experience in).

• Experience in building production-grade RAG applications and integrating with APIs.

• Familiarity with databases (e.g., PostgreSQL, MongoDB, Redis).

• Foundational programming knowledge (Git, Docker, OOP, FastAPI).

• Understanding of LLM architecture and practical applications.

Key Traits

• Strong problem-solving and systems thinking.

• Bias for action and comfort working in a fast-paced startup environment.

• Clear communication and collaborative mindset.

• Curiosity and openness to new ideas and technologies.

• Willingness to contribute beyond code – including product feedback, customer empathy, and roadmap planning.

Optional Requirements

• Familiarity with vector databases (e.g., Qdrant, Weaviate, Pinecone etc.).

• Experience building agentic workflows or using any frameworks like LangChain, Langgraph, CrewAI, AutoGen, Vercel SDK, OpenAI Agents SDK etc.

• Good understanding about transformers, LLMs, multi-agent systems, tool uses/function calling.

• Prior work with evaluation frameworks for LLM performance and safety.

What We Offer:

• A front-row seat to building the next generation of enterprise GenAI tooling and real-world applications.

• The opportunity to be part of a small but elite team of Asia's most promising GenAI startups.

• Exposure to real-world challenges and impactful deployments.

• A supportive and transparent team environment that values outcomes over hours. No politics, full focus on solving real problems.

• Growth opportunities across product, engineering, AI and R&D.

The Interview Process:

• There will be a total of 3 rounds of interview:

o 1st round: 30 minutes of general Q&A with the CEO

o 2nd round: 60 minutes technical session, where you will be given a real problem and is required to code out the solution within 30 minutes. Technical Q&A for the remaining time.

o 3rd round: 15 minutes for work arrangements and compensation package discussions.

ourteam is Southeast Asia's AI-powered hiring platform, helping companies hire faster, smarter, and without bias. We are looking for an
AI Engineer
to drive the development of our core AI capabilities.

This role will focus on building and scaling AI features that power next-generation hiring experiences. You will work closely with product and engineering teams to deliver production-ready AI that meets enterprise-grade standards for performance, reliability, and user experience.

Requirements

• Strong expertise in applied AI (NLP, LLMs, or Speech Processing)
• Proficiency in Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, etc)
• Experience deploying AI systems into production (preferably cloud environments)
• Ability to optimize models and systems for scale, latency, and reliability
• Strong problem-solving skills and ownership mindset

Benefits

• Opportunity to define and scale the AI backbone of an AI-first company
• Direct collaboration with founder and leadership team
• Work on applied AI that has immediate impact