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Singapore
Hybrid
SUMMARY
The Forward Deployed Engineer Lead Practitioner sets the standard for how Avanade Next turns business ambition into production reality. A player-coach responsible for the Forward Deployed Engineer track, this role combines business understanding, solution architecture and engineering leadership to help clients move from opportunity to deployed outcome. They work alongside executives, transformation leaders and domain specialists to understand strategic priorities, identify where AI can create measurable value, and translate those opportunities into production-grade solutions.
Beyond delivery, this role defines the AI-native engineering model for Avanade Next: small expert teams directing agent networks, accelerating delivery through reusable platforms and assets, and focusing engineering effort on solving business problems rather than writing code for its own sake. The role sets the technical standard, capability model and hiring bar for a track expected to scale from 20 engineers at launch to more than 100 practitioners over time.
KEY RESPONSIBILITIES
Partner with clients, Industry Reinvention Leads, Domain Architects, Value Architects and Trust Architects to understand business priorities, transformation initiatives and desired outcomes, and translate them into practical AI-enabled solutions.
Help clients identify where AI can create meaningful value, challenge assumptions where necessary, and shape solution concepts that are commercially viable, technically feasible and capable of operating at enterprise scale.
Bridge the gap between strategy and execution by converting business cases, reinvention opportunities and transformation roadmaps into production-ready solution architectures and delivery plans.
Own the engineering standard for the FDE track, including solution architecture patterns, engineering practices, testing, evaluation, production readiness and operational support requirements.
Define and continuously evolve AI-native engineering practices, including agent-assisted development, automated evaluation, reusable components, observability and production operations.
Define reference architectures, solution patterns and decision frameworks that help teams determine the most appropriate combination of Copilot Studio, Azure AI Foundry, Avanade Agentic Platform and broader Microsoft capabilities for a given business problem.
Set and maintain the hiring, assessment and capability-development standards for the FDE track, ensuring practitioners can operate effectively across business, architecture and engineering domains.
Lead the development of the FDE career pathway and enablement model, including structured re-skilling of traditional delivery engineers into AI-native Forward Deployed Engineers.
Deploy personally onto the most complex, strategic, at-risk or reference-critical mandates, helping clients translate ambitious transformation objectives into successful production outcomes.
Work closely with the AAP Platform Lead, IP & Products Lead and Delivery & Quality Lead to ensure solutions are built on repeatable foundations, contribute reusable assets back into the platform and meet Avanade Next production standards.
Partner with Value Architects to ensure solutions are linked to measurable business outcomes and with Trust Architects to ensure governance, risk, evaluation and adoption requirements are designed into solutions from the outset.
Contribute reusable solution patterns, reference architectures, engineering assets and delivery lessons from every major engagement to strengthen the Avanade Next platform, product portfolio and delivery model.
KEY MEASURES
Engineering standard and reference stack published and applied across builds within the first quarter.
Demonstrable productivity improvement across the FDE track through AI-native engineering practices and agent utilisation.
Production-grade delivery on every engagement; no prototype accepted without a production path.
Reusable components contributed per engagement, with measurable reuse across the track.
Retention of FDE talent.
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SKILLS AND EXPERIENCE
8+ years in software or AI engineering, with senior or lead accountability for systems that ran in production and were maintained afterwards.
Deep, current, hands-on agentic engineering: orchestration, retrieval, tool use, evaluation, observability and failure handling. Current practice matters more than years.
Strong command of the Microsoft AI stack; equivalent depth on another stack considered where the transfer is credible.
Has set an engineering standard for a team and made it hold — including with people who did not initially agree with it.
Experience assessing and hiring engineers at volume and at pace, with a defensible bar.
Client-facing credibility. This is a forward-deployed track and its lead is deployed too, not directing from the studio.
Comfortable building a capability where a large share of the bench arrives by internal transfer from traditional delivery and needs genuine re-skilling.
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