Principal Software Engineer, AI & Data Platform

Singapore (Hybrid)
Permanent
Salary: Up to SGD 220,000 per annum

We're partnering with an innovative technology company building next-generation AI-powered platforms for complex scientific and engineering applications. They are looking for a Principal Software Engineer, AI & Data Platform to lead the architecture of their data and AI engineering foundation, enabling scalable machine learning systems and intelligent workflows.

This is a highly hands-on technical leadership role for someone who enjoys solving difficult engineering problems while setting technical direction in a fast-moving, product-focused environment.

• Architect scalable data platforms capable of processing large volumes of scientific and engineering data, ensuring datasets remain reliable, reusable, and ML-ready.
• Design domain-specific data models that support analytics, automation, and machine learning workloads.
• Build high-performance data pipelines across object storage, analytical data platforms, and training-optimised datasets.
• Develop and maintain ML data pipelines covering data ingestion, curation, deduplication, feature preparation, evaluation datasets, and regression tracking.
• Build and operate model training and fine-tuning pipelines for production AI applications.
• Design intelligent workflow orchestration that connects user intent with platform capabilities through reliable AI-driven automation.
• Establish robust evaluation frameworks, benchmarking methodologies, automated scoring, and quality monitoring for AI models.
• Define engineering best practices across data and AI platforms, creating reusable frameworks, documentation, and technical standards.
• Work closely with cross-functional engineering teams to deliver scalable, production-grade AI systems.

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related discipline.
• 10+ years of experience designing and delivering production software systems.
• Expert-level Python development with a strong track record of building scalable backend platforms.
• Deep experience with large-scale data engineering, including object storage, analytical processing, distributed data pipelines, and training-optimised data formats.
• Hands-on experience building ML pipelines for model training, fine-tuning, evaluation, and continuous improvement.
• Experience fine-tuning or training AI models for structured outputs, workflow automation, tool usage, or domain-specific applications.
• Strong understanding of relational, document, and columnar database technologies and when to apply each.
• Experience working with cloud-native data platforms and distributed compute environments.
• Proven ability to make sound architectural decisions and thrive in ambiguous, early-stage product environments.
• Strong communication skills with the ability to influence technical direction across engineering teams.

• Experience applying machine learning to scientific, engineering, manufacturing, chemistry, materials, or simulation data.
• Experience with retrieval-augmented systems, knowledge graphs, hybrid search, or structured retrieval techniques.
• Experience designing APIs or platform interfaces for AI agents and intelligent workflows.
• Familiarity with workflow orchestration platforms and large-scale production ML infrastructure.
• Contributions to open-source projects in AI, data engineering, or scientific computing.