Intelligent Automation Engineering Lead

Delta Capita

4d•Hybrid

About The Position

We are looking for an experienced Intelligent Automation Engineering Lead to help accelerate the adoption of AI across engineering teams and client environments. This role is focused on enabling teams to successfully design, deliver, and scale AI solutions by providing technical leadership, guidance, and support across a variety of technology landscapes. You will work closely with Delta Capita engineering teams, client stakeholders, architects, and delivery teams to identify AI opportunities, shape solutions, and support the implementation of AI-enabled products and services. The role requires someone who is comfortable operating across cloud platforms, engineering disciplines, and enterprise systems, helping teams navigate both technical and operational considerations. Rather than being a traditional people management role, this position is suited to someone who enjoys collaborating with engineers, facilitating technical discussions, solving complex problems, and helping teams adopt practical AI solutions that deliver measurable business value.

Requirements

Strong background in Software Engineering, Platform Engineering, Cloud Engineering, Solutions Architecture, or Technical Consulting.
Experience delivering enterprise-grade technology solutions within large-scale environments.
Good understanding of Generative AI concepts including: AI Agents, Large Language Models (LLMs), Prompt Engineering, Context Engineering, Retrieval-Augmented Generation (RAG), Tool/Function Calling, Responsible AI Practices.
Experience supporting engineering teams through the full Software Development Lifecycle (SDLC).
Strong understanding of cloud platforms, particularly Azure and/or AWS.
Experience working with Python and modern engineering practices.
Strong communication skills with the ability to engage effectively with both technical and non-technical stakeholders.
Ability to balance innovation with practical business and operational requirements.
Experience working within Agile delivery environments.
A valid working permit for the UK is mandatory.

Nice To Haves

Experience building or supporting AI Agents, AI Assistants, Copilots, or AI Enablement Platforms.
Knowledge of MCP (Model Context Protocol) and enterprise AI integration frameworks.
Experience with AI governance, evaluation frameworks, observability, and LLMOps.
Experience with enterprise integration technologies, APIs, middleware, and event-driven architectures.
Hands-on experience across both Azure and AWS cloud environments.
Familiarity with GitLab CI/CD or similar DevOps toolchains.
Experience with observability and monitoring tools such as Splunk.
Experience within Financial Services, FinTech, Payments, or other highly regulated environments.
Understanding of security, risk, compliance, and governance frameworks supporting enterprise AI adoption.

Responsibilities

Partner with engineering teams to identify, assess, and deliver AI and automation opportunities across the organisation.
Provide technical guidance on Generative AI, AI Agents, LLMs, automation platforms, and emerging AI technologies.
Act as a trusted advisor to development, infrastructure, architecture, and product teams throughout the AI solution lifecycle.
Facilitate technical discussions between Delta Capita and client teams to align on architecture, delivery approaches, and AI adoption strategies.
Support engineering teams working across Python, Azure, AWS, infrastructure, and enterprise applications.
Review solution designs and provide recommendations on scalability, security, operational resilience, and maintainability.
Advise on AI governance, security, privacy, compliance, and responsible AI practices.
Help define and promote engineering best practices, standards, SDLC processes, governance frameworks, and reusable patterns to support the successful delivery of AI solutions.
Collaborate with Architecture, Security, CloudOps, and Product teams to ensure AI solutions align with enterprise standards.
Support teams throughout design, development, testing, deployment, and operational support phases.
Evaluate emerging technologies and identify opportunities to improve engineering productivity and business outcomes through AI.
Drive knowledge sharing and capability development across engineering teams.