We are working with one of the largest Singapore Banks on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation.

They are seeking an experienced Senior AI Engineer to join their team and drive the development of scalable, production-grade AI solutions.

This is full-time, on-site, 12-month contract based in Singapore.

Responsibilities:

AI/ML Development

Design, develop, and deploy machine learning models and AI solutions that solve complex business problems.
Build and implement agentic AI systems with autonomous decision-making, reasoning, and task execution capabilities
Develop multi-agent systems that use tools, planning, and memory.
Build and optimise large-scale ML pipelines for training, evaluation, and inference.
Implement state-of-the-art algorithms in areas such as natural language processing, computer vision, recommendation systems, or predictive analytics
Conduct experiments, perform model evaluation, and iterate on ML solutions to improve performance.
Stay current with the latest AI/ML research and identify opportunities to apply emerging techniques.

Software Engineering & Architecture

Design and implement robust microservices architectures for AI/ML applications
Build scalable, maintainable, and well-documented code across multiple programming languages.
Develop RESTful APIs and event-driven systems to integrate AI capabilities into broader platforms
Ensure high availability, fault tolerance, and performance optimisation of AI services.
Implement CI/CD pipelines for automated testing and deployment of ML models.

Technical Leadership

Mentor junior engineers and data scientists on best practices in AI engineering and software development
Lead technical design discussions and contribute to architectural decisions.
Collaborate with cross-functional teams, including product managers, data scientists, and platform engineers.
Conduct code reviews and promote engineering excellence across the team
Drive technical innovation and champion the adoption of new tools and methodologies

Required Qualifications

Technical Skills

AI/ML Expertise – 5+ years of experience developing and deploying machine learning models in production environments
Agentic AI – Experience building autonomous AI agents with capabilities including tool use, reasoning, planning, and multi-step task execution
Programming Languages – Advanced proficiency in at least Python and Java.
Experience with Deep learning frameworks (TensorFlow, PyTorch), data processing (pandas, NumPy), ML libraries (scikit-learn), etc.
Experience with Spring Boot, microservices frameworks, and concurrent programming.
Microservices Architecture – 5+ years of hands-on experience designing and implementing microservices-based systems
ML Operations – Experience with MLOps practices, model versioning, monitoring, and deployment strategies
Cloud Platforms – Proficiency with AWS, GCP, or Azure ML services and infrastructure

Additional Technical Requirements

Strong understanding of distributed systems, message queues (Kafka, etc.), and event-driven architectures
Experience with containerization (Docker) and orchestration (Kubernetes)
Knowledge of database systems (SQL and NoSQL) and data engineering concepts
Familiarity with API design principles and RESTful services
Experience with version control (Git) and collaborative development workflows

Experience

Bachelor's or master’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience).
Proven track record of delivering complex AI projects from conception to production
Strong problem-solving abilities and analytical thinking
Excellent communication skills with the ability to explain complex technical concepts to diverse audiences
Experience working in agile development environments

Preferred Qualifications

PhD in Machine Learning, Artificial Intelligence, or related field
Experience with agentic AI frameworks (LangChain, AutoGen, etc.) and agent orchestration patterns
Hands-on experience implementing Agents patterns such as ReAct, Plain and Execute, etc.
Hands-on experience with advanced prompting techniques such as Chain-Of-Thought, etc.
Experience with transformer architectures and large language models (LLMs)
Knowledge of vector databases and retrieval-augmented generation (RAG) systems
Contributions to open-source AI/ML projects
Publications in top-tier AI/ML conferences or journals
Experience with real-time inference systems and model optimisation techniques
Knowledge of federated learning, edge AI, or distributed training
Familiarity with MLflow, Kubeflow, or similar ML platforms
Experience with multiple cloud providers and multi-cloud architectures
Experience with building LoRA/QLoRA, PEFT pipelines for enterprise-specific tuning
Experience implementing distributed training pipelines (Ray, FSDP, DeepSpeed, PyTorch Distributed, etc.)
Experience with integrating continuous feedback loops to improve the model incrementally

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