AI SME (Subject Matter Expert)

MaxAccelerate AI Labs Division Location: UAE / Europe / Remote (Flexible) Reports to: Group CEO / Head of Innovation About MaxAccelerate AI Labs MaxAccelerate AI Labs is the innovation division of MaxAccelerate Technology Group, focused on designing and deploying next-generation AI automation ecosystems for enterprise clients. We are building intelligent AI Agents that integrate across CRM platforms such as Salesforce, Microsoft Dynamics, HubSpot and Zoho — transforming Sales, Recruitment, Customer Service and Executive Intelligence operations. Our objective is to build a catalogue of production-ready AI solutions and an AI Academy for enterprise deployment. We are now seeking an AI Subject Matter Expert (SME) to lead technical architecture, prototyping and deployment of AI agent systems. The Role This is a hands-on technical role. You will be responsible for: • Designing and deploying enterprise-grade AI agent systems • Building working prototypes and production-ready AI automations • Selecting and implementing best-in-class AI models and frameworks • Architecting multi-agent workflows • Integrating AI into CRM and business systems • Supporting the creation of modular AI solutions for repeatable deployment This is not a management-only role. We are looking for someone who actively builds. Key Responsibilities AI Agent Design & Deployment Design, build and optimise: • AI Sales Agents (SDR automation, lead qualification, CRM updates) • AI Customer Service Agents (ticket triage, response drafting, sentiment analysis) • AI Recruitment Agents (CV screening, candidate scoring, AI interviewers) • AI Executive Insight Agents (KPI summaries, reporting automation) Experience required in: • Multi-agent systems • Tool / function calling • Retrieval-Augmented Generation (RAG) • Memory systems and vector databases • Workflow orchestration for AI agents LLM & Model Experience Hands-on experience with: • OpenAI ecosystem (production or prototype deployments) • Anthropic Claude • Google Gemini • Azure OpenAI • Open-source LLMs (Llama, Mistral or similar) • Structured outputs and guardrails • Model selection based on use case, latency and cost CRM & System Integrations Experience integrating AI systems with: • Salesforce (preferred but not essential) • Microsoft Dynamics • HubSpot • Zoho Understanding of: • REST APIs • OAuth authentication • Webhooks • Automation tools (Make, Zapier, n8n or similar) Salesforce CRM experience is preferred but not essential. AI Data & Knowledge Architecture Experience with: • Vector databases (Pinecone, Weaviate, Chroma or similar) • Knowledge retrieval systems • Semantic data structuring • Business data modelling for AI use cases Conversational & Voice AI Experience with: • Conversational AI frameworks • AI voice agents • Speech-to-text and text-to-speech systems • Telephony or real-time AI integrations Governance & Responsible AI Knowledge of: • Data governance principles • Prompt injection mitigation • AI monitoring and evaluation • Responsible AI deployment Candidate Requirements • 12–24 months hands-on experience building AI agents or AI automation systems • Proven working prototype examples of AI agents already built and deployed (must be able to demonstrate) • Strong systems-thinking and architecture mindset • Comfortable operating in a fast-moving innovation environment • Commercial awareness and ability to translate technical solutions into business outcomes This role is suited to a builder who has already created real AI agents and wants to scale that experience into enterprise-grade deployments. What You Will Help Build • A full AI Agent Ecosystem • A catalogue of modular AI automation solutions • Enterprise-ready AI deployment frameworks • AI training and enablement programmes If you have already built AI agents and want to help architect and scale a dedicated AI Labs division, we would like to hear from you. Please apply with: • Your LinkedIn profile • A short summary of AI agents you have built • Links, demos or documentation of prototype systems

Candidate Requirements: 12–24 months hands-on experience building AI agents or AI automation systems. Proven working prototype examples of AI agents already built and deployed (must be able to demonstrate). Strong systems-thinking and architecture mindset. Comfortable operating in a fast-moving innovation environment. Commercial awareness and ability to translate technical solutions into business outcomes. This role is suited to a builder who has already created real AI agents and wants to scale that experience into enterprise-grade deployments.

The Role: This is a hands-on technical role responsible for designing and deploying enterprise-grade AI agent systems, building working prototypes and production-ready AI automations, selecting and implementing best-in-class AI models and frameworks, architecting multi-agent workflows, integrating AI into CRM and business systems, and supporting the creation of modular AI solutions for repeatable deployment. Key Responsibilities include AI Agent Design & Deployment (design, build and optimise AI Sales Agents, AI Customer Service Agents, AI Recruitment Agents, AI Executive Insight Agents), experience in multi-agent systems, tool/function calling, Retrieval-Augmented Generation (RAG), memory systems and vector databases, and workflow orchestration for AI agents. LLM & Model Experience requirements cover hands-on experience with OpenAI ecosystem, Anthropic Claude, Google Gemini, Azure OpenAI, open-source LLMs (Llama, Mistral or similar), structured outputs and guardrails, and model selection based on use case, latency and cost. CRM & System Integrations involve experience with Salesforce (preferred but not essential), Microsoft Dynamics, HubSpot, Zoho, and understanding REST APIs, OAuth, Webhooks, automation tools (Make, Zapier, n8n or similar). AI Data & Knowledge Architecture includes vector databases (Pinecone, Weaviate, Chroma or similar), knowledge retrieval systems, semantic data structuring, and business data modelling for AI use cases. Conversational & Voice AI coverage includes conversational AI frameworks, AI voice agents, speech-to-text and text-to-speech systems, and telephony or real-time AI integrations. Governance & Responsible AI encompasses data governance principles, prompt injection mitigation, AI monitoring and evaluation, and responsible AI deployment.