AI Delivery Analyst

About PhoenixDX

PhoenixDX builds mission-critical systems on the OutSystems low-code platform. As AI reshapes how software is delivered, we are investing in a new kind of practitioner - one who brings senior delivery discipline and a fully AI-integrated working practice to the entire analysis lifecycle.

What you will do...

The AI Delivery Analyst is not a traditional BA with AI tools bolted on. It is a combined discipline that compresses business analysis, UX design and test writing into a single role, operating with an orchestrated AI toolkit that enables a level of throughput and quality no traditional analyst structure can match.

You own the problem space from first stakeholder conversation to delivery acceptance. Across that span, you are accountable for:

Requirements & Discovery

• Lead stakeholder workshops, pain point analysis and requirements mapping on complex, enterprise-grade OutSystems engagements.
• Draft BRDs, process journeys and traceability artefacts in hours, not days, using AI-assisted analysis to surface gaps, implicit requirements and hidden dependencies.
• Maintain requirements integrity across long build cycles - ensuring traceability from business intent through to acceptance.
• Partner with architects and build teams on solution design and feasibility, holding the line on intent as requirements evolve.

Design & Prototyping

• Build high-fidelity UI designs in Figma, translating requirements directly into screen-level specifications the OutSystems build team can act on.
• Generate clickable prototypes for rapid stakeholder validation - removing the gap between specification and user feedback before a line of code is written.
• Keep requirements, architecture and design in continuous sync across the sprint cycle.

Quality & Testing

• Create test scripts from acceptance criteria as a natural output of story craft, not a separate downstream workstream.
• Deploy browser-based test agents to assess existing application state and triage defects against known issues.
• Flag coverage gaps, missing acceptance criteria and untested integration boundaries before sprint execution begins.

Delivery Flow & Backlog

• Lead backlog refinement using AI agents: propose refinements, group defects into clusters, validate with the Product Owner, and act.
• Produce development-ready stories - with design, acceptance criteria and test scripts - in a single integrated workflow.
• Drive sprint planning precision through AI-assisted dependency mapping and scope risk detection.

Capability Building

• Coach client team analysts in AI-augmented delivery practices, building durable capability that outlasts the engagement.
• Develop shared AI tooling playbooks: prompt patterns, workflow guides and quality checklists the team owns and evolves

Technical Depth & Architecture Contribution

• Engage meaningfully in solution architecture discussions - not as the architect, but as a practitioner who can challenge, question and contribute with informed technical perspective.
• Apply knowledge of software design principles and patterns (separation of concerns, modularity, API contracts, event-driven design) to identify when a proposed architecture will create downstream requirements risk or delivery friction.
• Review and stress-test technical design decisions from a requirements and usability standpoint, surfacing trade-offs the build team should resolve before they become defects.
• Contribute to OutSystems-specific design decisions: data modelling, service layer boundaries, integration patterns and screen architecture - with enough fluency to represent business intent accurately in technical conversations.

AI Tooling Currency

• Maintain an active, current awareness of the AI tools and agent ecosystems most relevant to delivery work - including Claude plugins and skills, Atlassian AI extensions, emerging LLM capabilities and low-code AI integrations.
• Continuously evaluate new tooling against the existing stack: assess what genuinely improves throughput or quality, trial it, and integrate it into the team’s workflow when it passes that bar.
• Act as the team’s forward radar on AI-augmented delivery practices - bringing relevant developments from the broader market to the engagement before they become common practice elsewhere.
• Contribute to PhoenixDX’s evolving AI delivery methodology: the prompts, patterns, tooling configurations and lessons learned that sharpen the practice across the organisation.

Who you are...

• 5+ years as a Business Analyst or Technical Analyst on complex, enterprise custom software delivery - not SaaS configuration or light-touch product work. You have held requirements integrity on systems that matter.
• An AI-integrated practice you have shaped deliberately. You can describe what you delegate to AI, what you share with it, and where you keep full human ownership - and why.
• Demonstrable experience with the tooling stack: LLM agents, Jira/Rovo, Figma and test automation tooling. You work with these daily, not occasionally.
• Discriminating judgement about AI outputs. You know when a result is right, plausibly wrong, or confidently wrong. You do not ship AI drafts without review.
• Strong communication across both ends of the room: senior business stakeholders and technical build teams. Calm, precise and non-alarmist under pressure.
• Familiarity with OutSystems or a comparable low-code rapid-application development platform.
• Sufficient technical depth to participate in architecture and solution design conversations - understanding software design patterns, integration principles and data modelling well enough to represent requirements accurately and challenge decisions that create delivery risk.
• A habit of staying current: you actively follow AI tooling ecosystems (including agent platforms, plugin marketplaces such as Claude skills, and low-code AI extensions), evaluate new capabilities against real delivery problems, and integrate what genuinely raises the bar.

Nice to Have's

• Experience in a software consultancy or professional services environment, working alongside client delivery teams.
• Background mentoring or upskilling analysts in AI-augmented practices.
• Familiarity with Agile scaling frameworks (SAFe, Scrum, or similar).
• Exposure to Tricentis, Lovable or agentic browser testing tools.
• Working knowledge of OutSystems architecture: service layers, integration patterns, screen architecture and data modelling.