AI Technical Lead

About Us

We are an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience.

Our values challenge us to do the best we can for ClearRoute, our customers and most importantly our team. We want to create a collaborative team to help build ClearRoute. This is an opportunity for you to build a consultancy from the ground up, use your voice to drive change and help transform organisations and problem domains.

Role

We need someone who can ship an enterprise AI platform that won't terrify the security team or hallucinate during board demos.

Our client is an enterprise level organisation with thousands of users. Multi-agent orchestration using ReAct patterns and Model Context Protocol. Not a chatbot. Not a PoC. Production system with real consequences. You'll own the technical delivery of a platform that fundamentally transforms how the organisation works internally and externally.

What You'll Do

• Lead 12-15 engineers across multiple teams
• Ship working agents to production within weeks, not quarters - Navigate Azure's quirks whilst building on LangGraph/LangChain
• Make and get buy-in for architectural decisions - Demo to C-level execs without making them panic about AGI
• Turn academic papers on agentic AI into working code

Technical Stack

Azure Services

• Azure Container Apps for agent orchestration
• Azure AI Search for vector indexing and semantic search
• Azure Cosmos DB for multi-model data (document, graph, key-value)
• Azure API Management for rate limiting and security policies
• Azure AI Foundry for model deployment and management
• Azure Functions for ETL pipelines and webhook processing
• Azure Key Vault for secrets management
• Azure Application Insights for observability
• Azure Blob Storage for document processing pipelines

AI/ML Technologies

• LangGraph agents with ReAct pattern orchestration
• Model Context Protocol (MCP) for tool integration
• LiteLLM/Portkey for model gateway routing
• RAG pipelines with hybrid search (vector + keyword)
• Langfuse for LLM observability
• Multiple LLM providers (Azure OpenAI, Anthropic, open models)

Enterprise Integrations

• SharePoint via Graph API (policy documents, QMS)
• ServiceNow for ITSM workflows
• Microsoft Teams/Entra ID for organisational data
• CIAM for external user authentication
• Zero-trust architecture with private endpoints everywhere

You Should Have:

• Built production LLM systems handling real workloads
• Led distributed teams through technical ambiguity
• Shipped when perfect wasn't possible (and can explain the trade-offs)
• Deep Azure experience - you know why Container Apps beats AKS for this use case
• Opinions on vector databases you can defend in an architecture review
• Comfort presenting to both engineers and executives in the same day
• Experience with enterprise authentication (OAuth, SAML, JWT tokens)