At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
Strategy and portfolio
Co-author and deploy the account Digital Strategy — vision, user needs, business benefits, technology, people and go to market — and improve it continuously.
Own the digital portfolio and its stage gates: promote, sustain, pivot or stop, prioritised on outcomes — more paid services, more margin — and report portfolio health.
Maintain the digital capability map for the customer and support the Client Partner in workshops, proposals and solutioning across software engineering, data management, AI development, AI agents and Agentic AI.
Product framing
Frame every idea into a product: vision, user needs, business case (more revenue, more margin), software and data architecture, and deployment infrastructure.
Internal products — prioritise with the leadership team on outcomes. External products — convert the framing into a customer proposal.
Software architecture, data and AI
Define and maintain the reference architecture and approved stack, including the no-code / low-code / full-code decision rule on criticality, lifespan and total cost of ownership.
Establish the data architecture and quality standard — data agent-ready at creation: structured, catalogued, machine-consumable.
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Define the AI reference patterns: use case archetypes, RAG and agents, human-in-the-loop, evaluation methodology, cost control, confidentiality guardrails.
Set the testing, quality and deployment standards that make software durable and adopted, and ensure compliance with the customer's security requirements and Quest Global IT and Information Security policy.
Deliver, prove and innovate
Make agentic coding tools (Claude Code, GitHub Copilot and equivalents) the standard way of working — including the training that makes the team genuinely productive — within the security, IP and review guardrails required on customer material.
Deliver AI-first proofs of concept personally, in days, as reference implementations of the architecture, data and test standards.
Scout emerging AI capability, run time-boxed experiments, and convert what works into the standard.
Team, skills and enablement
Design and stand up the digital team across five competencies — UI, Back End, Test, Data and AI — with roles, skill depth per level, sizing, operating model and competency paths.
Build AI as three capabilities, not one generic profile: AI application engineering (LLM integration, RAG, context engineering), agentic systems (orchestration, tool and API integration, automation), and AI evaluation and assurance (benchmarking, guardrails, human-in-the-loop, cost control).
Assess the current population against that structure; locate and attract top talent with Talent Acquisition, and be the technical face of the account in the market.
Own the people strategy: training path, AI fluency across the account, mentoring, community of practice, talent pipeline. Work through others by default.
We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.
We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.
Work Experience
Technical
Levels: Expert = exceptional depth, the reason we hire this person · Practitioner = does it personally · Experienced = has delivered and sets the standard · Knowledgeable = judges without building. Not every category needs depth.
Agentic coding — Expert: exceptional depth, single-user and multi-user — personal prompt-to-production workflow; multi-agent orchestration; shared context, instructions and standards across a team; agents in CI; review discipline for generated code at scale. Claude Code, GitHub Copilot or Cursor in daily use.
Data structure for AI applications — Expert: software whose data is agent-consumable from day one — schema-first not document-first, API and event access not manual export, semantic layer or ontology, metadata and lineage captured at creation, vector-ready content, automated quality gates. Every application built today must be a tool an agent can call tomorrow.
Testing — Expert: unit, integration and end-to-end automation, test data strategy, release gates, defect triage; and testing for AI features — evaluation sets, regression on non-deterministic output, human review in the loop.
Back end — Practitioner: REST / GraphQL API design, service decomposition, authentication and SSO, integration with SAP, SharePoint, PI and document systems; Python for engineering applications, and adaptability to whatever language a domain requires (for example Julia, Lua, MATLAB, Fortran).
AI — Practitioner: LLM API integration, RAG and vector stores, agent frameworks with tool / MCP integration, context engineering, evaluation and benchmarking, guardrails, human-in-the-loop, cost and latency control.
Product and portfolio — Practitioner: product vision, user needs, business case, definition of done, adoption, lifecycle and sunset; stage gates, outcome-based prioritisation, health reporting.
Data — Experienced: pipelines, modelling, warehouse / lakehouse, catalogue and lineage, quality rules; data platforms (Snowflake, Cognite, Palantir or equivalent); governance and stewardship — works directly with data owners, stewards and domain experts.
Front end (UI) — Experienced: SPA framework (React, Angular or equivalent), component library and design system, responsive dashboards, UX for dense engineering data; Figma and AI-assisted UI generation (Claude Design, Stitch, v0).
Knowledgeable — enough to set direction and arbitrate, not to build: infrastructure and deployment (Azure or AWS, containers, CI/CD, infrastructure as code, environments, monitoring, support model); low-code / no-code and BI (Power Apps, Power Automate, Power BI, and the no-code / low-code / full-code arbitration on total cost of ownership); security and compliance (enterprise information security, IP and confidentiality with AI tooling, customer security requirements).
Behavioural
Structuring and judgement — brings order, frames and standards to an unstructured landscape without bureaucracy; prioritises on outcomes and stops initiatives that have sponsors and sunk cost behind them.
Player-coach and enablement — builds personally to earn credibility, then grows capability in others and steps out of delivery; locates, attracts and technically assesses top engineering talent.
Communication and customer presence — excellent written and spoken English; conveys complex trade-offs clearly to non-technical executives and holds an architecture conversation with a customer's IT organization as a peer.
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