AI Software Architectural Leader

1 week ago

Singapore beBeeSoftware Full time $150,000 - $200,000
Job Overview

Achieve technical leadership as a software architect and drive the development of secure, scalable applications.

The role requires experience in Generative AI engineering, full stack development, DevSecOps & Shift-Left Practices, and leadership & collaboration. The ideal candidate will have 7+ years of professional software engineering experience, strong full stack development skills, and proven experience delivering Generative AI applications into production.

Key Responsibilities
  • Generative AI Engineering: Create LLM-powered applications using retrieval-augmented generation, multi-agent systems, generative search, and AI copilots. Implement prompt engineering, fine-tuning, and evaluation frameworks for production-grade reliability. Optimize AI inference at scale for latency, cost efficiency, and guardrails. Ensure responsible AI use with bias detection, explainability, and data privacy.
  • Full Stack Development: Architect and deliver end-to-end features across frontend, backend, 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 Qualifications:
    • 7+ years of professional software engineering experience.
    • Strong full stack development skills (Python, Java, etc.).
    • Proven experience delivering Generative AI applications into production, including LLM integration, RAG pipelines, fine-tuning, and evaluation of models.
    • 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 Qualifications:
    • 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.

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