Intelligent Automation Engineering Lead

Role: Intelligent Automation Engineering Lead
Employment Type: Fixed Term Contract – 6 Months
Location: Hybrid / Remote (London)

Role Overview:

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.

Key Responsibilities:

1. Partner with engineering teams to identify, assess, and deliver AI and automation opportunities across the organisation.

2. Provide technical guidance on Generative AI, AI Agents, LLMs, automation platforms, and emerging AI technologies.

3. Act as a trusted advisor to development, infrastructure, architecture, and product teams throughout the AI solution lifecycle.

4. Facilitate technical discussions between Delta Capita and client teams to align on architecture, delivery approaches, and AI adoption strategies.

5. Support engineering teams working across Python, Azure, AWS, infrastructure, and enterprise applications.

6. Review solution designs and provide recommendations on scalability, security, operational resilience, and maintainability.

7. Advise on AI governance, security, privacy, compliance, and responsible AI practices.

8. Help define and promote engineering best practices, standards, SDLC processes, governance frameworks, and reusable patterns to support the successful delivery of AI solutions.

9. Collaborate with Architecture, Security, CloudOps, and Product teams to ensure AI solutions align with enterprise standards.

10. Support teams throughout design, development, testing, deployment, and operational support phases.

11. Evaluate emerging technologies and identify opportunities to improve engineering productivity and business outcomes through AI.

12. Drive knowledge sharing and capability development across engineering teams.