AI Consultant / Solutions Engineer
This is a rare opportunity to join a specialized, ground-floor team working with early adopters to deploy AI voice agents and automation at scale. You will directly influence the platform's development, go-to-market approach, and client success.
The Role This is a dynamic, client-facing role that blends strategic technology consulting with hands-on technical delivery. You will work directly with clients to scope business problems, design architecture, and implement cutting-edge Agentic and Voice AI solutions.
Your day-to-day will involve:
Reference: SP-4064331
The Role This is a dynamic, client-facing role that blends strategic technology consulting with hands-on technical delivery. You will work directly with clients to scope business problems, design architecture, and implement cutting-edge Agentic and Voice AI solutions.
Your day-to-day will involve:
- Client Consulting: Leading pre-sales discovery workshops, assessing AI feasibility, and translating business requirements into clear technical roadmaps.
- AI Delivery: Building and deploying Voice AI agents, proofs-of-concept, and automation solutions tailored to client needs.
- System Integration: Developing Model Context Protocol (MCP) servers (in TypeScript or Python) to seamlessly connect AI agents to core business systems.
- Infrastructure & Operations: Supporting the deployment of AI workloads on on-premises GPU environments, and creating implementation guides and operational runbooks.
- 5+ years of experience in technical consulting, solutions engineering, or software integration, with a strong focus on AI, automation, or API-driven solutions.
- Hands-on AI Expertise: Practical experience with tools like LangChain, LlamaIndex, RAG architectures, vector databases, and LLM evaluation.
- Voice AI Knowledge: Familiarity with platforms like ElevenLabs, Vapi, Retell AI, or a solid understanding of conversational AI and telephony workflows.
- Development Skills: Ability to build MCP servers and robust integrations using Python or TypeScript, REST APIs, OAuth, and webhooks.
- Infrastructure Experience: Knowledge of deploying AI workloads on GPU environments, utilizing Docker and Kubernetes. (Cloud experience with AWS, Azure, or GCP is a bonus).
- Commercial Acumen: A background in client-facing delivery, managed services, or consulting, with a solid grasp of AI governance and privacy requirements (including the NZ Privacy Act).
Reference: SP-4064331