AI Engineer

CES ENGINEERING & CONSTRUCTION PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

CES ENGINEERING & CONSTRUCTION PTE. LTD. in Singapore seeks an experienced ML/Ops Engineer to develop, deploy, and productionize AI models for construction management tasks. You will build LLM-based solutions to process contracts, reports, and safety manuals, with edge deployment for on-site operation.

You will collaborate with civil and software engineers to ensure model quality, scalability, and reliable data pipelines, leveraging BIM/IFC workflows and modern MLOps practices.

Qualifications

• Recognized degree in Civil Engineering or Computer Engineering or equivalent Engineering field.
• Minimum of 3 years' of experience in building and deploying machine learning models in production.
• Experience in the AEC industry is desirable but not mandatory if you have a strong AI portfolio.
• Proficient in Python, Java or similar languages; strong coding practices with Git.

Responsibilities

• Model Development & Deployment: fine-tune LLMs for construction drawings recognition and predictive analytics.
• Data Engineering & Preparation: gather data from ERP, project management, and sensor networks; clean and transform data for training.
• Productization & MLOps: move models to production with scalable APIs and microservices for front-end apps used by PMs, Safety Officers, and QSs.
• Collaboration & Optimization: work with digitalisation engineers and end users to improve model accuracy, speed, and reliability.

Skills

Python

Java

Git

SQL

Education

Civil Engineering or Computer Engineering degree

Tools

TensorFlow

PyTorch

LangChain

LlamaIndex

Vector databases

BIM/IFC

Job description

Job Scopes

1) Model Development & Deployment

• Implement & fine tune Large Language Models for construction drawing component recognition, quantity take off.
• Develop predictive analytics models to forecast project risks, material delivery bottleneck and so on.
• Build and fine-tune Large Language Models (LLMs) or RAG (Retrieval-Augmented Generation) systems to process unstructured data like construction contracts, inspection reports, and safety manuals for instant Q&A and compliance checking.
• Optimize models for edge deployment on IoT devices or local servers on-site where internet connectivity is unreliable.

2) Data Engineering & Preparation

• Gather data from ERP systems and project management software, and sensor networks.
• Cleaning, labelling, and transforming large datasets into usable formats for model training

3) Productization & MLOps

• Move models from proof-of-concept to production, ensuring they are scalable, maintainable. Performance monitoring of the production environment.
• Build APIs and microservices to serve model predictions to front-end applications used by Project Managers, Safety Officers, and Quantity Surveyors.

4) Collaboration & Optimization

• Collaborating with digitalisation engineers & end users to refine models for better accuracy, speed, and reliability.

Requirements: (Qualification & Experience)

• Recognized degree in Civil Engineering or Computer Engineering or equivalent Engineering field or any other specialization relating to Construction Management.
• Minimum of 3 years' of experience in building and deploying machine learning models in a production environment.
• Experience in the AEC (Architecture, Engineering, and Construction) industry is highly desirable but not mandatory if you have a strong portfolio working with AI Models
• Programming: Proficient in Python, Java or similar languages. Strong coding practices in a collaborative environment (Git, code reviews).
• ML Frameworks: Deep experience with TensorFlow, PyTorch.
• Generative AI: Familiarity with LangChain, LlamaIndex, and vector databases for building RAG applications.
• Data Processing: Proficiency with SQL and data manipulation libraries.
• Experience with BIM (Building Information Modeling) software or IFC file formats.
• Stay current with the latest research in ConTech (Construction Technology) and apply cutting-edge techniques to improve safety, quality, and efficiency