AI Delivery Engineer 3

The Level 3 AI Delivery Engineer is a senior practitioner responsible for independently leading technology initiatives of moderate-to-high complexity across business analysis, requirements engineering, AI-enabled SDLC delivery, quality governance, and end-to-end traceability. Operating with full autonomy, the Level 3 engineer owns complete delivery programmes without day-to-day supervision and serves as the primary escalation point for Level 1 and Level 2 engineers facing complex technical, quality, and delivery challenges.
This role combines Technology Analyst and Quality Assurance Engineering responsibilities within a unified AI-enabled delivery model at an advanced level. Where the Level 2 engineer applies established frameworks, the Level 3 engineer creates, refines, and governs them. This includes architecting AI-enabled delivery workflows, designing and governing agent configurations, defining quality standards adopted by the broader team, and driving systematic improvement in TA and QA delivery practices across the function.
At this level, the engineer independently owns complex, multi-stakeholder AI delivery programmes and produces the full range of TA and QA artifacts at expert quality — including comprehensive BRDs for complex system changes, multi-stream user story packages, advanced acceptance criteria sets, cross-domain traceability frameworks, test strategies for complex programmes, and quality governance documentation for team-wide adoption. The Level 3 engineer drives AI-first delivery practices, accelerates team capability, and contributes meaningfully to the organisations AI delivery maturity

Business Analysis and Requirement Engineering

• Lead requirements engineering for complex, multi-team projects and programmes, independently managing ambiguous scope, competing priorities, and cross-functional stakeholder dependencies.
• Design and govern AI-enabled requirements workflows — including context model architecture, prompt framework design, and BRD generation standards — adopted by the broader team.
• Own senior stakeholder relationships across product, engineering, QA, and business domains; independently facilitate complex requirements workshops, decision sessions, and escalation meetings.
• Translate complex, multi-domain business intent into comprehensive AI-consumable models including advanced process flows, integration specifications, risk registers, and dependency maps.
• Define and enforce acceptance criteria quality standards, business rules completeness thresholds, and requirements traceability standards for team-wide adoption.
• Resolve ambiguous, conflicting, or incomplete requirements through independent analysis and targeted stakeholder engagement without escalation to senior leadership.

AI-Driven SDLC Orchestration

• Architect and own end-to-end AI delivery workflows for complex programmes spanning multiple workstreams, delivery phases, and cross-functional teams.
• Define agent sequencing, handoff protocols, human review gates, and validation checkpoints for multi-phase AI-enabled SDLC delivery at programme scale.
• Independently resolve AI workflow failures, quality exceptions, and cross-team delivery conflicts that Level 1 and Level 2 practitioners escalate; implement lasting remediation.
• Lead AI-assisted programme planning including backlog architecture, sprint sequencing, dependency resolution, and release planning for complex, multi-team initiatives.

AI Agent Development and Management

• Design, build, and maintain specialised AI agents for requirements analysis, workflow modelling, test design, automation generation, and quality validation use cases.
• Define agent interaction patterns, permission boundaries, escalation protocols, and output quality standards that the team adopts as governance baselines.
• Lead agent improvement cycles including prompt engineering, model evaluation, output benchmarking, and performance optimisation across the team's AI delivery agent ecosystem.
• Establish and govern agent registry management, version control practices, and performance baseline documentation for the team's AI delivery infrastructure.

AI Governance and Quality Assurance

• Design and own quality evaluation frameworks and acceptance thresholds for AI-generated artifacts across both TA and QA domains, adopted team-wide.
• Lead complex quality reviews for high-risk, high-priority deliverables; define and enforce team-level quality standards, governance protocols, and sign-off procedures.
• Identify systemic AI output errors, conduct root-cause analysis, and implement prompt engineering improvements and workflow corrections that benefit the whole team.
• Own AI risk, compliance, and auditability standards for the team; represent the delivery function in governance forums, audit processes, and compliance reviews.

Process Optimisation and Collaboration

• Lead process improvement initiatives across the TA and QA function; own delivery playbooks, SDLC automation standards, and quality governance frameworks for the team.
• Drive adoption of AI-first delivery practices across product, engineering, and QA teams; champion continuous improvement as a discipline, not a periodic activity.
• Mentor and develop Level 1 and Level 2 engineers; act as the primary technical escalation point for complex delivery, quality, and AI challenges across the team.
• Contribute to the team's AI delivery strategy, tooling evaluation, capability roadmap, and organisational AI maturity initiatives.

Domain

Expected Proficiency at Level 3

Business

Expert business analysis and AI context engineering. Independently leads complex multi-stakeholder requirements programmes; owns senior stakeholder relationships across product, engineering, QA, and business domains. Designs requirements frameworks, prompt libraries, and context models adopted by the broader team.

Technical

Expert-level SDLC, Agile programme management, and software quality strategy. Architects AI delivery workflows for complex, multi-team programmes; resolves advanced technical and delivery issues independently. Drives quality assurance standards and CI/CD integration for AI-generated artifacts.

AI & Automation

Expert prompt engineering across TA and QA use cases. Designs and builds AI agents; defines team-level agent governance and configuration standards; drives AI output quality improvement cycles; evaluates LLM performance and leads model selection decisions for delivery use cases.

Collaboration

Leads by influence; mentors and develops Level 1 and Level 2 engineers; drives team-level improvement initiatives and delivery standards. Owns senior stakeholder relationships; acts as the primary technical escalation point for complex delivery, quality, and AI challenges.

AI-Ready BRDs for complex, multi-team projects and programmes

Advanced User Story and Epic Packages with multi-domain acceptance criteria

AI Agent Design Specifications and Configuration Standards for team adoption

Quality Evaluation Frameworks and Governance Standards for TA and QA domains

Cross-domain Traceability Frameworks and Programme Traceability Maps

Delivery Playbooks, SDLC Automation Standards, and Process Improvement Roadmaps

Mentoring Plans and Technical Development Support for Level 1 and Level 2 Engineers

Education

• Degree: Bachelor's degree in Information Systems, Computer Science, Engineering, Business Administration, or a related field; master's degree preferred.

Required Experience

• Delivery experience: 4–7 years in technology analysis, business analysis, software quality assurance, AI-enabled software delivery, or related software delivery roles.
• Complex programme delivery: Demonstrated experience independently leading delivery across complex, multi-team, or multi-workstream initiatives without day-to-day supervision.
• AI delivery leadership: Proven experience designing AI-enabled delivery workflows, building or governing AI agents, and driving measurable improvements in team AI delivery maturity.
• Stakeholder ownership: Demonstrated experience owning senior stakeholder relationships across product, engineering, QA, and business domains; independently facilitated complex requirement sessions.

Required Skills

• Advanced requirements engineering: Expert-level BRD authoring, multi-domain process modelling, advanced acceptance criteria design, and cross-system traceability for complex, multi-team programmes.
• Business analysis leadership: Independently leads complex stakeholder sessions; resolves conflicting requirements and competing priorities without escalation; defines team requirements standards.
• AI workflow architecture: Designs and governs multi-step AI delivery workflows; defines agent sequencing, validation protocols, exception handling, and human review gate criteria.
• Prompt engineering (advanced): Designs prompt frameworks, context model architectures, and knowledge library structures for team-wide adoption and continuous improvement.
• Quality governance: Designs quality evaluation frameworks; defines and owns quality standards and acceptance thresholds for both TA and QA artifact types adopted by the team.
• Communication (senior): Produces executive-ready documentation, programme-level status reports, and governance artifacts for senior stakeholder and leadership consumption.
• Technical leadership: Acts as primary escalation point for complex delivery and AI challenges; mentors junior practitioners; identifies and drives team capability development opportunities.