VP Engineering — AI Platform & Infrastructure

We’re looking for a VP of Engineering to lead the development, architecture, and reliability of our Enterprise SaaS platform for AI Digital Human and Conversational AI applications. This is a hands-on role. You will be expected to code, design architecture, manage cloud and DevOps practices, troubleshoot production issues and mentor junior developers.

Key Responsibilities Lead the architecture and development of scalable and secure Enterprise SaaS applications.  Develop and maintain backend services using Node.js, TypeScript, Express.js or NestJS.  Design APIs, integrations, databases, and cloud infrastructure.  Manage deployments and infrastructure across AWS and GCP.  Establish strong CI/CD, monitoring, logging, and DevOps practices.  Monitor production systems using AWS CloudWatch, Datadog, and similar tools.  Troubleshoot application, infrastructure, database, and performance issues.  Manage and optimise MySQL databases.  Integrate LLMs, RAG, vector databases, STT/TTS, and Voice AI services.  Conduct code reviews and mentor junior and mid-level developers.  Define engineering standards for security, scalability, code quality, and reliability.

Required Skills & Experience 8–10+ years of software engineering experience, primarily in backend, platform, or Enterprise SaaS development.  At least 5 years in a technical lead, engineering lead, or similar leadership role.  Strong hands-on experience with Node.js, TypeScript, Express.js or NestJS.  Strong experience building and operating Enterprise SaaS platforms.  Good understanding of SaaS architecture, APIs, distributed systems, and backend design.  Strong experience with GCP and/or AWS.  Solid DevOps experience including Docker, CI/CD, deployment, monitoring, logging, and incident management.  Experience with AWS CloudWatch, Datadog, or similar observability tools.  Strong knowledge of MySQL and database optimisation.  Experience with Git, code reviews, testing, and software engineering best practices.  Experience leading and mentoring engineering teams.  Willing and able to code regularly and remain technically hands-on.

Good to Have Experience with Kubernetes, Terraform, Redis, message queues, or WebSockets.  Experience with multi-tenant SaaS architecture and enterprise security.  Experience with LLMs, RAG, AI Agents, Chatbots, Voicebots, or Digital Human applications.