Project description:

We are looking for a Senior AI Engineer to design, build, and deploy enterprise-grade multi-agent AI solutions on Microsoft Azure. The role focuses on developing structured, production-ready AI workflows that support research, sales, relationship management, and CRM automation processes.

Ability to maintain a minimum of four hours of daily overlap with London business hours (09:00–18:00 GMT/BST) and willingness to travel to London periodically for key project milestones and workshops.

Tech stack:

• Microsoft Azure & Microsoft Fabric

• Azure AI Foundry & Azure OpenAI Service

• Python

• AI Agent Frameworks (Semantic Kernel, AutoGen, LangGraph)

• MCP (Model Context Protocol)

• Azure DevOps / GitHub Actions

• Monitoring & Observability (Azure Monitor, Application Insights)

• Third-party integrations (DealCloud, BuytheSell)

Requirements:

• Strong background in Software Engineering, AI Engineering, or a related technical discipline.

• Proven hands-on experience delivering production-grade Agentic AI and LLM-powered solutions.

• Demonstrated experience designing, building, and deploying multi-agent AI systems in enterprise environments.

• Strong expertise in agent orchestration frameworks such as Azure AI Foundry Agent Service, Semantic Kernel, AutoGen, LangGraph, or similar.

• Experience building AI solutions using Azure OpenAI Service and/or Anthropic Claude models.

• Strong understanding of agent coordination, state management, context propagation, routing, handoffs, and failure handling.

• Hands-on experience developing MCP-based solutions and natural language interfaces for enterprise data.

• Strong experience with Azure services including Azure OpenAI, Azure AI Foundry, Azure Functions, Service Bus, API Management, and App Services.

• Experience integrating AI solutions with Microsoft Fabric lakehouses, warehouses, and data pipelines.

• Knowledge of event-driven architectures, asynchronous processing, and message-based workflows.

• Experience implementing secure and governed AI solutions, including RBAC, VNet integration, private endpoints, and enterprise compliance standards.

• Strong Python programming skills.

• Experience with CI/CD pipelines using Azure DevOps and/or GitHub Actions.

• Ability to work independently in complex and evolving business environments.

• Experience collaborating with architecture, security, and governance teams.

• Availability for at least 4 hours of overlap with London business hours.

• Willingness to travel periodically to London.

Nice to have:

• Experience with Copilot Studio agents or Microsoft 365 Copilot extensions.

• Experience integrating AI solutions with DealCloud, Salesforce, or similar CRM platforms.

• Experience with BuytheSell or other research and sales workflow platforms.

• Background in financial services, research, deal management, or relationship management domains.

• Experience implementing AI-driven scoring, targeting, engagement, or workflow automation solutions.

• Knowledge of Microsoft Purview for governance, compliance, and data lineage.

• Experience with Azure Monitor, Application Insights, or similar observability platforms.

• Understanding of Responsible AI, AI governance frameworks, and guardrail implementation.

• Experience with TypeScript.

• Experienced in using AI tools in day-to-day workflow

Main responsibilities:

• Design and implement production-grade multi-agent AI systems supporting business workflows.

• Build and orchestrate agent interactions using Azure AI Foundry, Semantic Kernel, AutoGen, LangGraph, or equivalent frameworks.

• Develop deterministic, auditable, and observable AI workflows with proper state management.

• Implement agent routing, handoffs, context propagation, and failure recovery mechanisms.

• Design and develop MCP-based solutions and natural language interfaces over enterprise data.

• Build AI-accessible tools and MCP server components exposing business capabilities and data sources.

• Integrate AI solutions with Microsoft Fabric lakehouses, warehouses, APIs, and data pipelines.

• Develop integrations with DealCloud, BuytheSell, and other enterprise systems.

• Implement secure and governed access patterns, including RBAC and enterprise security controls.

• Build event-driven AI services using Azure Functions, Service Bus, and API Management.

• Establish monitoring, observability, audit trails, and AI governance controls.

• Define deployment, versioning, rollback, and lifecycle management standards for AI agents.

• Collaborate with architecture, security, and compliance teams to ensure alignment with enterprise requirements.

• Produce technical documentation, reusable architectures, and knowledge transfer materials.

• Drive end-to-end delivery from solution design through production deployment and handover.