AI Product Engineer

Selby Jennings; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Selby Jennings seeks an experienced backend engineer to own AI-driven product platforms from concept to production deployment. You will architect robust systems, integrate models, and ensure scalable data pipelines with strong emphasis on reliability and performance.

Beyond core backend work, you will explore retrieval-based and agent-based AI approaches, while collaborating closely with product, engineering, data, and operations teams to maintain high-quality, accountable software delivery.

Qualifications

• Minimum 3 years of professional software engineering experience, ideally with end-to-end product delivery.
• Strong backend fundamentals including system architecture and API design.
• Exposure to infrastructure, frontend integration, deployment workflows, and product performance metrics.

Responsibilities

• Build and maintain backend systems for AI applications, including model integration and data movement.
• Define technical approaches and maintain engineering standards across design, development, testing, and code reviews.
• Create safeguards and monitoring for dependable AI products, including guardrails and fallback logic.
• Own production readiness: monitoring, troubleshooting, incident management, and quality improvements.
• Assess AI approaches (prompt engineering, retrieval augmented generation, fine tuning, agent-based workflows).
• Collaborate with product, engineering, data, operations, and end users to drive delivery.

Skills

Backend systems

Distributed systems

Databases

Caching technologies

Messaging systems

APIs

LLM applications

Stakeholder collaboration

Ownership mindset

Job description

• Work closely with product stakeholders to build and launch AI driven products from early concept and experimentation through to production deployment, performance tracking, and continuous improvement.
• Take ownership of the backend systems supporting AI applications, including model integration, retrieval pipelines, APIs, data movement, storage, orchestration layers, and supporting services.
• Define and influence the technical approach for the products and systems you own, making sound architecture decisions and maintaining strong engineering standards across design, development, testing, and code review.
• Build the technical safeguards required for dependable AI products, including evaluation frameworks, fallback logic, guardrails, human review processes, and robust handling of model or system failures.
• Maintain ownership of products after launch, including production monitoring, troubleshooting, incident management, root cause analysis, and ongoing quality improvements.
• Assess and apply the most suitable AI approach for each use case, whether through prompt engineering, retrieval augmented generation, fine tuning, agent based workflows, or traditional machine learning techniques.

Job Requirements

• Minimum 3 years of professional software engineering experience, ideally with exposure to building products or platforms from the ground up and taking them through the full development lifecycle.
• Strong backend engineering fundamentals, including system architecture, distributed systems, databases, caching technologies, messaging systems, and API design.
• Comfortable working beyond the backend when required, with exposure to infrastructure, frontend integration, deployment workflows, and product performance metrics.
• Practical experience building LLM enabled applications such as conversational products, copilots, retrieval based systems, or agentic workflows, with the ability to make informed decisions around model usage and system design.
• Able to work confidently with existing codebases and contribute across commonly used backend programming languages and frameworks.
• Strong stakeholder management and collaboration skills, with the ability to work effectively across product, engineering, data, operations, business teams, and end users.
• Adaptable, curious, and comfortable working in an environment where requirements and technologies may evolve quickly.
• Strong ownership mindset with a consistent focus on delivery quality, reliability, and accountability.