Intelligent Automation Engineering Manager

Role - Intelligent Automation Engineering Manager Employment Type - Fixed term contract - 6 months Mode - Hybrid/ Remote Role Overview: We are seeking an Intelligent Automation Engineering Manager to lead a high-performing AI Enablement engineering team focused on accelerating the responsible adoption of AI across the enterprise. The successful candidate will drive the delivery of enterprise-grade AI agents, AI enablement platforms, and integration capabilities, while providing technical leadership, people management, and strategic direction across a rapidly evolving AI ecosystem. This role will partner closely with Product, Architecture, Security, Compliance, CloudOps, and Integration teams to ensure AI solutions are secure, scalable, well-governed, and deliver measurable business value. Key Responsibilities: • Lead, mentor, and develop a team of engineers, fostering a culture of technical excellence, innovation, and continuous improvement. • Drive the delivery of AI enablement capabilities that support the adoption of Generative AI and agentic workflows across the organisation. • Own the roadmap, strategy, and engineering delivery of the MCP ecosystem, enabling secure integration between AI assistants, enterprise systems, data sources, and business applications. • Lead the design, development, deployment, and operational support of AI agents and AI-powered solutions. • Establish engineering standards and best practices for AI architecture, orchestration, retrieval, tool invocation, observability, governance, privacy, security, and cost management. • Review technical designs and architecture documentation to ensure solutions align with engineering, security, and governance standards. • Translate emerging AI opportunities into pragmatic delivery plans balancing innovation, scalability, reliability, and business outcomes. • Collaborate with integration, automation, platform, and cloud teams to ensure AI solutions integrate effectively with existing enterprise systems and workflows. • Partner with Product, Architecture, Security, Compliance, and Business stakeholders to ensure successful delivery and adoption of AI solutions. • Support Azure architecture decisions and work closely with CloudOps and InfoSec teams to ensure secure and scalable platform delivery. • Manage delivery planning, risks, dependencies, and stakeholder communications at both operational and executive levels. • Promote agile delivery methodologies, engineering best practices, reusable frameworks, and automation across the AI Enablement function. Essential Skills & Experience • Proven experience leading and developing high-performing Software Engineering, Platform Engineering, Automation, or AI Engineering teams. • Strong people leadership experience, including coaching, mentoring, performance management, and career development. • Experience delivering enterprise software platforms, developer tools, automation solutions, or AI-enabled products in production environments. • Strong understanding of Generative AI concepts, including: • AI Agents • LLMs • Prompt Engineering • Context Engineering • Tool/Function Calling • Retrieval-Augmented Generation (RAG) • Responsible AI practices • Experience leading discussions around system architecture, APIs, integrations, event-driven systems, security, scalability, and operational resilience. • Strong stakeholder management skills with the ability to influence Product, Architecture, Security, Compliance, and Business teams. • Excellent communication and presentation skills across both technical and non-technical audiences. • Experience delivering solutions within Agile software development environments. • Passion for building practical and scalable AI solutions that drive measurable business outcomes. Desirable Experience • Experience building or operating AI Agents, AI Assistants, Copilots, or AI Enablement Platforms. • Experience with MCP (Model Context Protocol), MCP Servers, MCP Clients, or enterprise AI integration frameworks. • Knowledge of LLMOps, AI evaluation frameworks, model routing, and AI observability tooling. • Experience with enterprise integration technologies including APIs, Middleware, ESB, or iPaaS platforms. • Hands-on experience integrating internal and third-party systems. • Azure cloud experience, including: • Azure OpenAI • Azure AI Foundry • Azure App Services • Azure Functions • Azure Kubernetes Service (AKS) • Azure API Management • Azure Networking & Security • Experience with GitLab CI/CD or similar DevOps tooling. • Experience with Splunk or other observability platforms. • Previous software engineering or development background. • Experience within Financial Services, FinTech, Payments, or highly regulated environments. • Exposure to governance, privacy, risk, and security frameworks supporting enterprise AI adoption.How We Work:  Delta Capita is an equal opportunity