Lead Developer: AI & Automation at Zhone… · Hanoi

We are seeking a Lead Developer with expertise in AI/ML/LLM system design and cloud-native architectures to lead the design, development, and delivery of scalable AI-powered applications. This role will play a critical part in architecting end-to-end solutions that leverage modern LLM frameworks, agentic AI, and cloud infrastructure, while partnering closely with engineering and product teams from concept through production.

Key Responsibilities:

• Architect, design, and deliver AI/LLM-powered cloud-native applications from initial concept through production deployment.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including integration with vector databases.
• Lead architectural decisions for agentic AI systems, including deployment, orchestration, and scalability.
• Develop and maintain architecture diagrams, technical design documents, and AI/ML white papers.
• Build and deploy applications on Kubernetes-based cloud platforms, ensuring reliability, scalability, and security.
• Lead development across teams, work with inter-org development teams, build and test solutions for release to customers.
• Collaborate with cross-functional teams to translate business requirements into robust technical solutions.
• Work with large datasets, including data preparation, model training, and fine-tuning workflows.
• Evaluate and integrate emerging AI technologies, tools, and protocols into the platform architecture.
• Ensure best practices across cloud infrastructure, AI governance, observability, and performance optimization.
• Work with teams in the US, Europe and India and comfortable working different timezones.
• Support triaging customer bugs and fixes.

Required Qualifications:

• 5+ years of experience in software engineering, systems architecture, or a related technical role, with hands-on experience designing, developing and delivering AI/LLM-driven, cloud-native applications.
• Proven experience building cloud-native applications deployed on Kubernetes.
• Hands-on experience designing and implementing RAG pipelines and working with vector databases.
• Experience using MCP or A2A protocols.
• Experience building and deploying agentic AI agents in production environments.
• Strong background working with large datasets, including model training and fine-tuning.
• Proven track record of delivering applications end-to-end, from design through production.

Nice to Have:

• Experience building highly scalable SaaS platforms.
• Exposure to multi-tenant architectures and enterprise-grade AI solutions.
• Experience working in cross-functional or distributed engineering teams.