AI Engineer

PHOENIX SOLUTIONS (S) PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

PHOENIX SOLUTIONS (S) PTE. LTD. is seeking an experienced ML engineer to design, develop and deploy machine learning solutions and services in a production setting.

You will implement end-to-end ML pipelines from data ingestion to training and model serving, and operationalize LLMs and multi-agent systems. The role requires strong Python skills, hands-on experience with Docker and Kubernetes, and familiarity with ML tooling and CI/CD processes.

Qualifications

• Bachelor's or master's degree in data science, CS, math, stats or related field.
• Proficient in Python with clean, testable code.
• Experience with Docker for containerization and Kubernetes for orchestration.
• GPU/cloud compute awareness for ML workloads.
• Hands-on ML tooling: MLflow or equivalent.
• Experience with LLMs and AI agentic workflows.
• Familiar with CI/CD pipelines (GitLab, Jenkins).
• Strong math and statistics foundation.

Responsibilities

• Design, develop and deploy ML solutions and services.
• Implement end-to-end ML pipelines from data ingestion to training and model serving.
• Operationalize LLMs, embeddings, and multi-agent systems.

Skills

Python

Docker

Kubernetes

MLflow

LLMs

CI/CD

Data preprocessing

Model evaluation

DevOps

Problem solving

Education

Bachelor's or Master's degree in data science, Computer Science, Mathematics, Statistics, or a related field

Tools

Weights & Biases

Job description

Functional / Technical

• Design, develop and deploy machine learning solutions and services
• Implement end-to-end machine learning pipelines from data ingestion to training and model serving
• Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
• Manage the machine learning and model lifecycle (experimentation, registry, deployment)
• Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
• Containerize applications using Docker and orchestrate them via Kubernetes
• Build and maintain CI/CD pipelines for ML models and LLM applications
• Collaborate with data scientists to refactor research code into production-ready Python code
• Monitor model performance, data drift, and performance in production
• Assess and integrate AI solutions ensuring optimal performance and reliability
• Design and implement production grade RAG systems
• Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes
• Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

SKILLS REQUIREMENTS

• Bachelor's or master’s degree in data science, Computer Science, Mathematics, Statistics, or a related field
• Advanced proficiency in Python programming with a focus on writing clean, testable, and efficient code
• DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
• Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
• MLOPS tools: 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)
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization
• Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively
• Relevant work experience in machine learning, data science or a related field