Lead AI Software Architect

2 weeks ago

Singapore beBeeSoftware Full time $150,000 - $180,000
Job Title

Achieve Technical Leadership as a Software Architect.

About the Role

We seek an experienced technologist to lead our software development team and drive architecture, development, and deployment of secure, scalable applications.

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 (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 Qualifications:

  • 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:
    • 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 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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