Principal Software Engineer, AI & Data Platform
Summary
Lead the architecture of a data and AI platform for scalable ML systems, building pipelines, orchestration, and evaluation frameworks for production AI applications in a scientific domain.
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.
What You'll Do 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.
What We're Looking For
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.
Nice to Have
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.
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.
What You'll Do 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.
What We're Looking For
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.
Nice to Have
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.