AI Field Delivery Engineer

Context

Our customer establishing a dedicated AI team to integrate structured AI engineering capabilities into its software delivery organization. The team is divided into two functions: a Platform team that builds shared infrastructure, and an Enablement team that collaborates with engineering and product teams to deliver AI use cases and transfer the capability for independent operation. The objective is to achieve measurable AI capability across the SDLC within six months.

This role is part of the Enablement team and works directly with engineering and product teams across the delivery organization to scope, build, and ship AI use cases, then hand over ownership. The engagement concludes when the team can operate and iterate on the solution without support. The primary success metric is the graduation rate.

Missions

• Select and scope use cases by collaborating with engineering and product stakeholders to identify well-defined, AI-appropriate problems with measurable outcomes.

• Lead delivery efforts alongside a contributor from the delivery team, ensuring active involvement throughout the process.

• Manage stakeholders including sponsor relationships, defining success metrics, production launch, and handover.

• Transfer capability by enabling teams to explain, maintain, and extend the delivered solutions.

• Provide field feedback by reporting patterns, friction points, and tooling gaps back to the platform team.

• Coordinate technically with the LLM engineer on patterns and architecture.

Tools & Environment

• Collaboration with engineering and product teams within a software delivery organization.

• Engagement with LLM engineers on technical decisions related to TypeScript, RAG pipeline design, observability traces, and agentic systems.

• Use of AI-powered systems and workflows involving LLM-based agents and tool-calling flows.

Working Conditions

• Role embedded within the Enablement team of our customer’s AI organization.

• Focus on delivering AI use cases and enabling independent AI capability across multiple teams.

• Success measured by the number of use cases shipped and teams independently delivering AI work within six months.

1. Experience in AI delivery within a software organisation, shipping AI-powered systems while collaborating closely with engineering and product stakeholders
2. Technical credibility including ability to read and write TypeScript, understand RAG pipeline design, interpret observability traces, and engage with LLM engineers on technical decisions
3. Working familiarity with LLM-based agents, tool-calling flows, or AI-assisted workflows in a product context
4. Problem scoping ability to identify well-suited AI solutions and redirect or re-scope engagements when necessary
5. Strong communication skills to run effective kickoffs, manage scope, surface issues early, and provide clear written communication
6. Handover orientation with a track record of building delivery capability in teams rather than creating ongoing dependency