New opportunity

Full Stack AI Engineer Associate Director

Accenture · Singapore

About this role

The Full Stack AI Engineer Associate Director leads enterprise-scale agentic AI transformation for Accenture clients in Singapore. This senior, technically engaged leader works independently with CXOs and board-level sponsors to identify business problems, define AI strategy and business cases, and direct the design and delivery of full-stack AI solutions. The role combines architecture oversight, programme and commercial accountability, client growth, and leadership of Accenture’s Full Stack AI practice.

Responsibilities

Lead executive workshops, analyse business processes, develop value hypotheses and financial models, establish measurable outcomes, and prioritise transformation roadmaps and organisational adoption.
Set architecture principles for agent harnesses, orchestration, knowledge layers, trust and safety, and AgentOps. Govern standards for agent-to-agent patterns, RAG, MCP, Text-to-SQL, knowledge graphs, evaluation using golden datasets, LLM-as-judge and trajectory evaluations, and production readiness. Assess emerging models, frameworks and tools for enterprise use.
Oversee concurrent programmes across scope, quality, risk, milestones, margins, change control, client satisfaction and measured business value. Grow strategic client accounts, lead major pursuits and commercial negotiations, and identify new markets for agentic AI.
Hire and develop senior leaders and engineers; build practice capabilities, reference architectures, delivery frameworks and reusable tools. Contribute thought leadership, continuous learning and strategic partnerships with platform vendors, hyperscalers and AI framework providers.

Required qualifications

14+ years of full-stack engineering and software delivery; 12+ years in classical AI/ML, data engineering and advanced analytics; 10+ years in cloud-native development; and 10+ years of technical and delivery leadership.
3+ years delivering production agentic AI systems with verifiable enterprise business value, and 3+ years building LLM applications. Requires current depth across the agentic AI stack and the ability to direct senior engineers and govern portfolio architecture.
Demonstrated CXO-level engagement, enterprise AI transformation leadership, complex software delivery in regulated or multinational environments, sales and account growth, and senior engineering hiring and development. Bachelor’s degree in a related field.

Additional strong signals include a master’s degree in Computer Science, AI or Engineering, or an MBA; external agentic AI thought leadership; AI practice scaling; strategic partner relationships; experience across at least two regulated industries; global programme delivery; enterprise AI economics; and large-scale organisational change.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Agentic AILarge Language ModelsAI agentsAgent orchestrationAgentOpsRAGModel Context ProtocolAgent-to-agent protocolsText-to-SQLKnowledge graphsLLM evaluationLLM-as-judgeTrajectory evaluationTrust and safetyAI architectureMachine learningData engineeringAdvanced analyticsFull-stack engineeringCloud-native developmentDevOpsEnterprise architectureTechnical leadershipPortfolio managementFinancial modellingBusiness developmentDigital transformation

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn executive problems into measurable AI programmes
The role leads CXO and board-level discussions, diagnoses business processes, builds financial cases, and prioritises roadmaps by value and readiness. Prepare to explain how a baseline, value hypothesis, measurable outcomes, and adoption plan connect.
Set production-grade agentic AI direction
The role sets architecture principles and governs portfolio standards for orchestration, knowledge layers, evaluation, trust and safety, and AgentOps. Prepare a concrete example of an architecture decision, its trade-offs, and the evidence used to judge production readiness.
Protect delivery and commercial outcomes across programmes
The role oversees concurrent programmes and owns milestones, margins, change control, risk, client satisfaction, and measured value. Prepare to describe how portfolio visibility leads to timely escalation and course correction.
Grow strategic client relationships
The role develops accounts into longer-term partnerships, leads major pursuits and negotiations, and identifies new agentic AI opportunities. Prepare to connect a credible solution and demonstrated impact to a proposed commercial opportunity, without assuming every use case is ready.
Build the Full Stack AI practice
The role hires and develops senior leaders and engineers while creating reference architectures, delivery frameworks, value-engineering tools, and reusable accelerators. Prepare to distinguish what should be standardised across teams from what must remain client-specific.

Likely challenges to think through

Balancing innovation with production assurance
Inferred preparation hypothesis: rapidly changing models and frameworks may offer new capabilities before their enterprise readiness is clear. Prepare a decision framework comparing demonstrated value with evaluation results, safety controls, operating requirements, and architectural fit.
Proving value beyond deployment
Inferred preparation hypothesis: a working agentic system may not deliver its financial case if processes and adoption do not change. Prepare to show how baseline measures, post-deployment outcomes, and an adoption roadmap would test whether the promised value materialised.
Managing competing portfolio pressures
Inferred preparation hypothesis: protecting engineering quality and client outcomes across concurrent programmes may conflict with milestones, scope, and margin. Prepare an example of how to surface the trade-off, govern a change decision, and communicate it to senior stakeholders.

Practice your answers

Technical practice

How would you turn an executive workshop into a scoped agentic AI opportunity?

What to coverSuggested preparation (inferred): Explain how you would analyse the current process, identify root causes and decide whether an agentic solution addresses them. Describe how you would define the end-to-end solution and test whether the opportunity is ready to pursue.

How would you construct and validate an enterprise AI business case?

What to coverSuggested preparation (inferred): Cover the value hypothesis, current-state baseline, financial model and measurable outcomes. Explain how you would track realised value through delivery and after deployment rather than relying on projected benefits alone.

What architecture principles would you set for agent harnesses and orchestration?

What to coverSuggested preparation (inferred): Discuss how you would divide responsibilities among agents, the harness and orchestration, including when an agent-to-agent pattern is justified. Explain the trade-off between flexibility and the control needed to govern quality across a portfolio.

How would you choose among RAG, Text-to-SQL and knowledge graphs?

What to coverSuggested preparation (inferred): Relate each knowledge-layer option to the information and questions it must support, and explain where a combination would be warranted. Address data quality, access controls and how you would check that retrieved or generated answers are grounded.

How would you govern MCP-enabled tools and agent trust and safety?

What to coverSuggested preparation (inferred): Explain how you would set boundaries on tool access and agent actions within an enterprise architecture. Discuss the trade-off between useful autonomy and controls appropriate to the business risk.

How would you evaluate an agentic system before production release?

What to coverSuggested preparation (inferred): Describe distinct uses for golden datasets, LLM-as-judge and trajectory evaluations, including their limitations. Explain how evaluation results would inform production-readiness decisions and subsequent improvements.

What would your AgentOps and production-readiness standards cover?

What to coverSuggested preparation (inferred): Discuss operational visibility, failure handling and engineering quality for cloud-native agentic applications. Explain how you would apply consistent standards across programmes without overlooking differences in their risks.

How would regulated or multinational constraints shape an agentic AI architecture?

What to coverSuggested preparation (inferred): Explain how compliance, data residency and AI governance could affect data flows, knowledge access and deployment choices. Distinguish requirements that should be common across a portfolio from those needing local decisions.

How would you assess a new model or framework for enterprise use?

What to coverSuggested preparation (inferred): Describe a comparison against the programme's requirements for capability, evaluation results, operational fit and trust and safety. Explain what evidence would justify adoption rather than changing technology solely because it is new.

How would you prioritise an AI roadmap while weighing build, buy and partner options?

What to coverSuggested preparation (inferred): Compare strategic value and execution readiness across initiatives, then consider total cost of ownership and the capabilities needed to deliver each one. Explain how organisational adoption and investment governance would affect the sequence.

Behavioural practice

Tell me about a time you influenced a senior sponsor's technology decision.

What to coverSuggested preparation (inferred): Use a real situation, your actions and the result to show how you understood the business concern and handled disagreement. Make clear what evidence you presented and how the decision was reached, without overstating your authority.

Describe a time you corrected a programme facing delivery and commercial risks.

What to coverSuggested preparation (inferred): Use a real situation, your actions and the result to explain how you assessed scope, quality, milestones or margin and chose an intervention. Address change control and how you communicated with the client.

Tell me about a pursuit or account-growth decision you led.

What to coverSuggested preparation (inferred): Use a real situation, your actions and the result to explain how you shaped the opportunity, solution and commercial position. Clarify your own contribution and the evidence of client value, without assuming every pursuit succeeded.

Describe how you developed senior engineering leadership or practice capability.

What to coverSuggested preparation (inferred): Use a real situation, your actions and the result involving hiring, developing leaders or establishing reusable engineering standards. Explain how you assessed whether the capability improved delivery rather than merely producing artefacts.

Tell me about a time you led adoption through significant organisational change.

What to coverSuggested preparation (inferred): Use a real situation, your actions and the result to show how you addressed stakeholder concerns and changes to work or processes. Explain how you assessed sustained adoption and what you would do differently.

Sharpen your knowledge

Topics to prepare

Enterprise agent architecture
The posting explicitly calls for architecture principles covering agent harnesses, orchestration, A2A patterns, trust and safety, and AgentOps. Revision exercise: compare two orchestration approaches for a hypothetical enterprise workflow, explaining control boundaries, failure modes, and production trade-offs.
Knowledge-layer design
The posting names RAG, MCP/tools, Text-to-SQL, and knowledge graphs. Revision exercise: map which of these approaches would serve different information needs in a hypothetical client process, and explain their data-access and governance trade-offs.
Evaluation and production readiness
The role governs golden datasets, LLM-as-judge, trajectory evaluations, and production-readiness criteria. Revision exercise: define what each evaluation method would test for one agent workflow, including how you would investigate a failed trajectory.
Enterprise AI value and adoption
The posting requires value hypotheses, financial modelling, current-state baselines, measurable outcomes, roadmaps, and sustainable adoption. Revision exercise: outline a hypothetical business case that connects a process problem to a baseline, expected value, measurement plan, and adoption risks.
Portfolio, commercial, and practice governance
The role owns concurrent programme delivery, margins, change control, client growth, and practice standards and hiring. Revision exercise: work through a hypothetical portfolio escalation and distinguish the delivery decision, commercial decision, and reusable practice standard it might prompt.

Skills to revise

Facilitate an executive problem-framing workshop
Practice a short CXO-level workshop: elicit a process bottleneck, test its root cause, and close with a value hypothesis and measurable outcome. This reflects the posting’s executive engagement and forward-deployed problem diagnosis; the scenario is a preparation exercise, not a stated client project.
Critique a full-stack agent design
Draw a hypothetical solution spanning the harness, orchestration, tools, knowledge layer, safety controls, and operations; then defend two consequential trade-offs as if reviewing senior engineers’ work. The posting explicitly expects hands-on depth to govern portfolio architecture.
Draft an evaluation and release review
For a hypothetical agent workflow, specify golden-dataset cases, LLM-as-judge checks, trajectory review, and production-readiness decisions. This practices the evaluation standards and engineering-quality governance named in the posting.
Build and challenge a value model
Create a simple hypothetical baseline, benefit estimate, cost assumptions, and post-deployment tracking plan; stress-test the assumptions before presenting an investment recommendation. This directly supports the role’s business-case, financial-modelling, and outcome-measurement responsibilities.
Manage a programme escalation
Rehearse a concise decision brief for a hypothetical programme facing a missed milestone and scope change: state quality and risk impacts, margin and change-control options, client communication, and a recommended course correction. These are explicit portfolio accountabilities in the posting.
Shape a pursuit and practice capability
Draft a brief hypothetical proposal outline, then identify the reference architecture, delivery standard, and senior-engineering capability needed to deliver it. This practices the posting’s pursuit leadership, account growth, practice building, and team-development responsibilities.

Your preparation checklist

Map evidence to the core requirements
The posting specifies experience across engineering, AI/ML and data, cloud-native delivery, leadership, production agentic AI, LLM applications, CXO engagement, sales, hiring, and a related bachelor’s degree. Prepare a one-page evidence map with dates and specific examples; leave gaps unclaimed.
Prepare a production agentic AI walkthrough
Select an actual deployment, if available. Prepare a concise account of the business problem, your decisions, production operations, and verifiable enterprise value, distinguishing your contribution from the team’s.
Rehearse an executive transformation discussion
The role leads CXO workshops and builds business cases. Prepare a sample workshop agenda and value hypothesis showing current-state baselines, financial assumptions, measurable outcomes, roadmap priorities, and adoption considerations. Label the sample as a preparation exercise.
Work through an enterprise architecture exercise
Sketch a hypothetical full-stack agentic solution and explain trade-offs in harness design, orchestration, A2A and MCP/tools, RAG or other knowledge approaches, trust and safety, and AgentOps. Include golden datasets, LLM-as-judge or trajectory evaluation, and production-readiness criteria.
Document portfolio and commercial decisions
Prepare an example of governing concurrent delivery across scope, risk, quality, milestones, margin, change control, and client outcomes. Show what triggered an escalation and how you measured the result, using only decisions and figures you can substantiate.
Prepare a pursuit and account-growth example
The role leads proposals, senior client relationships, and commercial negotiations. Outline an actual pursuit or account-growth example, if available, covering the client need, solution positioning, your role, commercial trade-offs, and outcome.
Show how you would build the practice
Prepare examples of senior hiring and development, plus a brief proposal for reusable reference architectures, delivery standards, or value-engineering tools. Distinguish work you have done from ideas you would propose for this role.
Bring questions about priorities and authority
Ask how Accenture expects this Associate Director to balance hands-on technical direction, portfolio accountability, client growth, and practice building; how production value is measured; and where architecture and commercial decisions sit.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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