Total 2 job vacancies by Matrix Process Automation Pte. Ltd.

Development and delivery with Java, EJB, SpringBoot, MySQL, Oracle and IBM MQ.

Understand business / system requirements andtranslate them into technical solutions that are scalable, stable and fit forpurpose.

Tackle very complex legacy defects thatmediocre developers cannot analyse/solve.

Ensure adherence to the bank standards (includingcode, security and performance).

The role provides exposures to WealthManagement systems.

Passionate about technologies and buildingrobust and scalable Java Systems.

Analysis, design coding, and implementationof complex and custom-built applications.

Work collaboratively with team to achievegoals. Experience working in Agile methodologies, such as Scrum, and/or Extremeprogramming.

Experience in following in following bestpractices like Test-Driven Development (TDD) and continuous Integration (CI)

Must Have:

Core Java 8+ and JUnit.

Spring Boot

Strong computer science knowledge (algorithms, data structures,object-oriented design)

TDD. Able to write acceptance test.

Maven

Basic Linux shell scripting commands

Java Messaging Concepts. (JMS, MQ)

Relational Database concepts, SQL, joins, index, triggers etc.

Web Services Rest, SOAP.

Spring core concepts.

• Design, develop and deploy machine learningsolutions and services
• Implement end-to-end machine learningpipelines from data ingestion to training and model serving
• Operationalize LLMs, embeddings, andmulti-agent systems in real-world applications
• Manage the machine learning and modellifecycle (experimentation, registry, deployment)
• Oversee the model promotion lifecycle,coordinating validation gates and approval workflows to safely deploy new modelversions from stating to production
• Containerize applications using Docker andorchestrate them via Kubernetes
• Build and maintain CI/CDpipelines for ML models and LLM applications
• Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
• Proficient with Docker for containerization and working knowledge of Kubernetes for orchestration
• Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
• Hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
• Proven experience working with Large Language Models (LLMs)
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
• Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
• Hands-on experience with CI/CD pipelines (GitLab, Jenkins)