Full Stack Engineer, AI

TechKnowledgey Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

TechKnowledgey Pte Ltd is seeking a Full Stack Engineer - AI to design and build production-grade workflows that translate core intelligence into usable features. You will connect frontend interfaces with backend services, integrating AI inference pipelines and memory layers to deliver reliable user experiences.

You will collaborate with ML, backend, and product teams to ship high-impact features, focusing on scalable architecture, low-latency interfaces, and robust monitoring and recovery

Qualifications

• Experience building full-stack features across modern frontend and backend.
• Solid understanding of scalable system design and robust API architecture.
• Hands-on experience with large language models, RAG, or AI-powered products.
• Able to navigate ambiguity and make pragmatic engineering tradeoffs.
• Proven ownership from concept to production deployment.

Responsibilities

• End-to-End Product Engineering: Architect and build features across the stack.
• Agent Workflow Design: Create agent workflows with multi-step planning and recovery.
• LLM & Tool Integration: Integrate LLMs and external tools into production systems.
• Real-Time AI Interaction: Build interfaces with streaming responses and low latency.
• Reliability & Observability: Improve monitoring, fallback, and reliability.

Skills

Full stack

System architecture

AI/LLM experience

Decision making

Ownership execution

Tools

Kubernetes

Docker

Next.js

PyTorch

OpenAI API

Anthropic API

SQL/NoSQL

Open-Source LLMs

Job description

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