TECHKNOWLEDGEY PTE. LTD. Hiring! Full Time Full Stack Engineer, AI in Central Region (Singapore), Earn up to SGD 8,000 - Ricebowl

We are seeking a Full Stack Engineer - AI to build the product layer that translates core intelligence capabilities into usable, production-grade workflows. This role encompasses designing how autonomous agents operate, handle failures, execute recovery sequences, and deliver consistent, dependable value to users across every interaction.

You will engineer end-to-end product features spanning frontend interfaces, backend orchestration services, and deep AI integrations, ensuring seamless operation under real-world conditions.

Core Responsibilities

• End-to-End Product Engineering: Architect and build robust features across the entire stack, connecting modern frontend clients with backend services and AI inference pipelines.
• Agent Workflow Design: Design sophisticated agent workflows capable of managing multi-step planning, tool utilization, failure detection, and automated recovery across sessions.
• LLM & Tool Integration: Integrate large language models, persistent memory layers, and external software tools into cohesive systems that behave reliably in production.
• Real-Time AI Interaction: Build responsive, real-time user interfaces supporting streaming responses, partial results, and strict latency constraints.
• Reliability & Observability: Continuously improve system reliability, monitoring, observability, and fallback mechanisms for non-deterministic model components.
• Cross-Functional Collaboration: Partner closely with machine learning, backend, and product teams to ship high-impact features seamlessly.

Ideal Experience & Background

• Full Stack Proficiency: Strong, demonstrated experience in full-stack engineering across modern frontend and backend frameworks.
• System Architecture: Solid understanding of scalable system design, distributed patterns, and robust API architecture.
• AI & LLM Application Experience: Practical experience working with large language models, retrieval-augmented generation (RAG) systems, or AI-powered product applications.
• Pragmatic Decision-Making: Demonstrated ability to navigate ambiguity, exercise strong technical judgment, and make pragmatic engineering tradeoffs.
• Ownership & Execution: Strong sense of ownership with a proven track record of taking complex features from initial concept through to production deployment.

Technical Stack

• Frontend: Next.js
• Languages: Python, Node.js
• Machine Learning & Frameworks: PyTorch, OpenAI API, Anthropic API, Open-Source LLMs
• Data Stores: SQL and NoSQL database systems
• Infrastructure & Orchestration: Kubernetes, Docker