Lead Software Engineer (AI, Artificial Intelligence)

JOBSTER PRIVATE LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading tech company in Singapore is seeking a Lead Software Engineer to lead the development of AI-native products. In this role, you will combine robust engineering with cutting-edge generative capabilities, drive architecture, and mentor engineers. The ideal candidate has over 7 years of experience, strong skills in full stack development, and a background in Generative AI and DevSecOps practices. This position offers an opportunity to innovate in a fast-paced environment.

Qualifications

• 7+ years of professional software engineering experience.
• Strong full stack development skills including React, Node.js, Python, and Java.
• Proven experience delivering Generative AI applications into production.

Responsibilities

• Design and build LLM-powered applications such as generative search and intelligent agents.
• Architect and deliver end-to-end features across frontend and backend.
• Embed security and testing early in the development lifecycle.

Skills

Full stack development

Generative AI

DevSecOps practices

Tools

React

Node.js

Python

Java

Docker

Kubernetes

AWS

GCP

Azure

Job description

Overview

We are seeking a Lead Software Engineer with deep expertise in full stack development, Generative AI, and DevSecOps practices. This is a hands-on leadership role at the forefront of building AI-native products that combine robust engineering with cutting-edge generative capabilities.

You will drive the architecture, development, and deployment of secure, scalable applications infused with AI — from LLM-powered experiences and intelligent agents to automated workflows and decisioning systems. Beyond technical execution, you will embed shift-left quality and security, mentor engineers, and set the direction for how Generative AI shapes our product strategy.

Key Responsibilities

• Generative AI Engineering
  • Design and build LLM-powered applications (retrieval-augmented generation, multi-agent systems, generative search, AI copilots).
  • Implement prompt engineering, fine-tuning, and evaluation frameworks for production-grade reliability.
  • Optimize AI inference at scale (latency, cost efficiency, guardrails).
  • Ensure responsible AI use (bias detection, explainability, data privacy).
• Full Stack Development
  • Architect and deliver end-to-end features across frontend (React/Next.js), backend (Node.js/Python/Java), and cloud-native infrastructure.
  • Build secure APIs, data pipelines, and real-time services that power AI-driven user experiences.
• DevSecOps & Shift-Left Practices
  • Embed security and testing early in the development lifecycle.
  • Own CI/CD pipelines with automated testing, vulnerability scanning, and compliance guardrails.
  • Implement Infrastructure as Code (IaC) and observability for resilience and scalability.
• Leadership & Collaboration
  • Mentor engineers on AI software engineering best practices.
  • Collaborate with product managers and data scientists to translate business problems into AI-first solutions.
  • Drive technical decision-making, balancing innovation speed with long-term system sustainability.

Qualifications

• Must-Have:
• 7+ years of professional software engineering experience
• Strong full stack development skills (React/Next.js, Node.js, Python, Java, etc.)
• Proven experience delivering Generative AI applications into production, including:
• o LLM integration (OpenAI, Anthropic, Llama, etc.)
• RAG pipelines (vector databases, embeddings, semantic search)
• Fine-tuning and evaluation of models
• Safety/guardrail implementation
• Solid knowledge of DevSecOps and shift-left practices (automated testing, SAST/DAST, IaC, CI/CD).
• Hands-on experience with cloud-native platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes) and infrastructure provisioning.
• Strong foundation in software architecture and distributed systems.
• Nice-to-Have:
• Experience with multi-agent architectures or autonomous AI workflows.
• Familiarity with AI evaluation frameworks (LangSmith, Weights & Biases, MLflow).
• Contributions to open-source AI/ML or developer tooling.
• Startup or high-growth environment experience.