About us

Established in 2000 and headquartered in Singapore, iHub Solutions is a Logistics Technology (LogTech) firm providing third-party logistics services across Malaysia, Hong Kong, the Philippines and Thailand. Our warehousing and last-mile fulfilment services are powered by a proprietary technology stack, featuring our Virtual Logistics System (VLS) WMS and Cloud Transport System (CTS) TMS. Beyond standard 3PL, we act as a software integrator and e-commerce enabler, seamlessly connecting logistics data with ERP systems and e-commerce platforms to provide greater visibility and smarter operations.

Key Responsibilities

Applied AI Solution Development

Design and develop machine learning and AI solutions for operational and business use cases.
Build solutions involving computer vision, OCR, barcode processing, document extraction, predictive modelling, optimisation and Generative AI.
Select appropriate modelling, rules-based or hybrid approaches based on business requirements, data availability, risk and expected operational value.
Develop proofs of concept and convert suitable prototypes into maintainable production applications.

Production Engineering

Build, test, deploy and maintain end-to-end machine learning applications and inference services.
Develop reliable APIs and backend services using FastAPI, Flask or equivalent frameworks.
Package and deploy applications using Docker and suitable deployment pipelines.
Write modular, maintainable and documented Python code, supported by automated tests and code review.
Investigate and resolve production issues involving models, data, integrations, application code or infrastructure.

Data, Evaluation and Reliability

Collect, validate, clean and transform structured and unstructured operational data.
Develop data pipelines supporting model training, evaluation and production inference.
Create representative evaluation datasets and define metrics covering accuracy, false acceptance, false rejection, latency, reliability and manual-review effort.
Perform error analysis and maintain reproducible experiments, datasets, model versions and evaluation results.
Implement input validation, logging, exception handling, retry behaviour, fallback mechanisms and human-review workflows.
Monitor data quality, model behaviour, service latency, resource usage and system availability.

Business and System Integration

Work directly with business users to understand workflows, constraints and failure consequences.
Translate ambiguous operational requirements into clear technical requirements and acceptance criteria.
Integrate AI capabilities with warehouse, transport, enterprise and internal business applications.
Coordinate with software developers, infrastructure personnel, vendors and operational teams.
Measure whether deployed solutions reduce manual effort, improve accuracy or deliver other demonstrable operational benefits.

Responsible Use of AI Development Tools

Use AI-assisted coding and development tools where they improve productivity.
Independently review, understand, test and validate AI-generated code and technical recommendations.
Identify security, privacy, licensing, reliability and maintainability risks associated with generated code or third-party AI services.

Required Qualifications

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering or a related discipline, or equivalent practical experience.
3+ years of relevant experience in machine learning, applied AI or ML-oriented software engineering.
Demonstrated ownership of at least one machine learning or AI solution deployed for real users.
Strong Python and SQL skills with sound software-engineering fundamentals.
Experience developing and deploying APIs using FastAPI, Flask or an equivalent framework.
Hands-on experience with Docker, Git, automated testing and CI/CD workflows.
Experience with at least one major ML framework, such as PyTorch, TensorFlow or scikit-learn.
Experience preparing, validating and analysing real-world operational data.
Ability to design suitable model and system evaluations and investigate failures across the full solution stack.
Experience deploying or supporting applications in at least one cloud or on-premises environment.
Ability to communicate technical concepts, risks and trade-offs clearly and work independently through ambiguous problems.

Preferred Qualifications

Experience with computer vision, OCR and document processing using OpenCV or similar tools.
Experience developing language-model applications involving retrieval, structured output, tool calling or agent workflows.
Experience evaluating language-model outputs and implementing validation, guardrails or human review.
Familiarity with MLflow, application observability and production incident investigation.
Experience deploying applications on Windows Server and Linux.
Familiarity with Google Cloud Platform (GCP), infrastructure-as-code or automated environment provisioning.
Experience in logistics, warehousing, supply chain, manufacturing or industrial automation.
Familiarity with route, scheduling or constraint-optimisation problems.

Applied AI & Machine Learning Engineer (Mid/Senior)