Artificial Intelligence Engineer

Charterhouse Partnership; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Charterhouse Partnership seeks a Senior AI Solutions Engineer to design, build and deploy enterprise-grade AI systems across GenAI, ML and AI Agents for mobility and transportation operations worldwide.

The role sits within the core AI and engineering team, collaborating with data scientists, architects and product teams to deliver end-to-end AI solutions from concept to production deployment with global rollout, embedding MLOps, FinOps and responsible AI practices throughout the lifecycle.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence or a related technical field.
• 3+ years of hands‑on experience in AI/ML model development, generative AI, AI Agents and end‑to‑end AI solution deployment to production.
• Proven expertise in building MLOps pipelines, with hands‑on experience in CI/CD, model monitoring, drift detection and cloud‑native AI deployment.
• Strong technical skills in LLM fine‑tuning, RAG pipeline development, vector search and prompt engineering, with experience integrating AI models into business applications.

Responsibilities

• Design, prototype and deliver advanced AI systems and pipelines for GenAI, ML, AI Agents and agentic architectures, tailoring solutions to the unique needs of global mobility and transportation operations
• Build production-grade MLOps pipelines encompassing data ingestion, feature engineering, model training, evaluation, optimisation and deployment, with full CI/CD, versioning, monitoring, drift detection and rollback capabilities
• Develop, fine-tune and integrate LLMs and ML models into business applications, including building robust RAG pipelines, vector search functionality and effective prompt engineering strategies
• Design and optimise cloud-agnostic AI architectures across AWS, Azure, GCP and VMware using containerisation, creating reusable APIs, microservices and AI components for consistent global deployment
• Apply FinOps principles with guardrails and quotas to optimise AI models and pipelines for performance, latency, cost and scalability, ensuring predictable and efficient cloud spend across global operations
• Embed explainability, responsible AI practices and governance controls into all AI solutions, ensuring compliance with global regulatory standards, data privacy laws and company risk frameworks
• Define and track AI system KPIs, building monitoring, alerting and performance dashboards to measure model reliability, business impact and operational efficiency, driving continuous improvement of AI solutions
• Mentor junior engineers, champion best practices in modern AI development and delivery, and build a strong internal AI community to uplift the team’s overall AI engineering capability

Skills

GenAI

ML model development

LLM fine-tuning

RAG pipelines

Vector search

CI/CD

Cloud platforms

FinOps

Responsible AI

Education

Bachelor’s or Master’s in CS/AI

Tools

AWS

Azure

GCP

VMware

Job description

A technology organisation is seeking a Senior AI Solutions Engineer to design, build and deploy enterprise-grade AI systems across generative AI, machine learning and agentic architectures, driving the global adoption of scalable, high-performance and cost-optimised AI solutions for its mobility and transportation operations.

This role sits within the core AI and engineering team, working alongside data scientists, architects, systems analysts and product teams to deliver end-to-end AI solutions, from concept and proof-of-concept through to production deployment and international rollout, while embedding MLOps, FinOps and responsible AI practices into every stage of the AI development lifecycle.

Responsibilities:

• Design, prototype and deliver advanced AI systems and pipelines for GenAI, ML, AI Agents and agentic architectures, tailoring solutions to the unique needs of global mobility and transportation operations
• Build production-grade MLOps pipelines encompassing data ingestion, feature engineering, model training, evaluation, optimisation and deployment, with full CI/CD, versioning, monitoring, drift detection and rollback capabilities
• Develop, fine-tune and integrate LLMs and ML models into business applications, including building robust RAG pipelines, vector search functionality and effective prompt engineering strategies
• Design and optimise cloud-agnostic AI architectures across AWS, Azure, GCP and VMware using containerisation, creating reusable APIs, microservices and AI components for consistent global deployment
• Apply FinOps principles with guardrails and quotas to optimise AI models and pipelines for performance, latency, cost and scalability, ensuring predictable and efficient cloud spend across global operations
• Embed explainability, responsible AI practices and governance controls into all AI solutions, ensuring compliance with global regulatory standards, data privacy laws and company risk frameworks
• Define and track AI system KPIs, building monitoring, alerting and performance dashboards to measure model reliability, business impact and operational efficiency, driving continuous improvement of AI solutions
• Mentor junior engineers, champion best practices in modern AI development and delivery, and build a strong internal AI community to uplift the team’s overall AI engineering capability

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence or a related technical field
• 3+ years of hands‑on experience in AI/ML model development, generative AI, AI Agents and end‑to‑end AI solution deployment to production
• Proven expertise in building MLOps pipelines, with hands‑on experience in CI/CD, model monitoring, drift detection and cloud‑native AI deployment
• Strong technical skills in LLM fine‑tuning, RAG pipeline development, vector search and prompt engineering, with experience integrating AI models into business applications
• Familiarity with cloud platforms (AWS, Azure, GCP, VMware), containerisation and modern deployment patterns, with a solid understanding of FinOps principles for cloud cost optimisation
• Deep knowledge of responsible AI practices, AI governance frameworks and global data privacy laws (GDPR, CCPA) and compliance requirements
• Excellent cross‑
• Experience in the mobility, transportation or logistics industry is highly desirable, with a track record of delivering AI solutions that drive tangible operational gains

EA License no.: 16S8066 Rep no.: R25157345

Only successful applicants will be notified.