New opportunity

Forward Deployed AI Engineer

Accenture · Singapore

About this role

Accenture seeks a Forward Deployed AI Engineer for its Health and Public Services portfolio in Singapore. This hands-on, client-facing role designs and implements autonomous and semi-autonomous agentic systems for government and public sector clients. The engineer works alongside delivery teams and clients to turn solutions into repeatable capabilities across engagements.

Responsibilities

Design, build and deploy single- and multi-agent systems to automate aspects of public service delivery.
Develop reference architectures, tools and reusable patterns for public sector, defense and public safety work.
Build evaluation frameworks, observability tools and guardrails to make agent behavior measurable, safe and reliable in production.
Establish governance for autonomy levels, human-in-the-loop checkpoints and escalation protocols, particularly in regulated environments.
Select and integrate technologies suited to each use case, including agent harnesses, IDE-based agents, cloud agents and orchestration frameworks. Coach delivery teams on agentic engineering practices.

Required qualifications

4–8+ years of software engineering experience and a strong record of delivering production systems. Candidates need excellent communication and stakeholder management skills, the ability to work independently in ambiguous client-facing settings, and hands-on experience developing LLM-based agentic systems involving tool use, function calling, multi-step reasoning and orchestration. High proficiency in Python, Java, React and/or TypeScript, plus experience designing evaluation and guardrail mechanisms for non-deterministic AI systems, is required.

Preferred qualifications

Experience with agent frameworks and harnesses, vector databases, RAG and orchestration tools; consulting, solutions engineering or technical delivery leadership; reusable capabilities or internal tools that scale across teams; delivery for government or public sector clients; and familiarity with public sector technology stacks and security policies for public service, defense and public safety environments.

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

Skills for this role

Agentic systemsMulti-agent systemsLarge language modelsTool useFunction callingMulti-step reasoningAgent orchestrationPythonJavaReactTypeScriptAI evaluationAI guardrailsObservabilityHuman-in-the-loop systemsReference architecturesVector databasesRAGStakeholder managementSecurity posture

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

Deploy working agentic systems
Design, build and deploy single- and multi-agent systems that automate aspects of public service delivery. The role calls for production engineering experience and hands-on LLM agent work involving tool use, function calling, multi-step reasoning and orchestration.
Make behavior measurable and reliable
Build evaluation frameworks, observability tools and guardrails for non-deterministic agents operating in production.
Govern agent autonomy
Define autonomy levels, human-in-the-loop checkpoints and escalation protocols, especially for regulated public-sector environments.
Turn delivery work into reusable capabilities
Develop reference architectures, tools and patterns that can scale across engagements in public service, defense and public safety contexts.
Work directly with clients and delivery teams
Operate independently in ambiguous, client-facing settings; select technologies suited to each use case and coach teams on agentic engineering practices.

Likely challenges to think through

Balancing automation with oversight
Likely preparation challenge, not a confirmed employer problem: greater agent autonomy may speed delivery but increase the need for human review and escalation. Prepare to explain how you would set autonomy boundaries for a public-service workflow.
Proving reliability despite variable outputs
Likely preparation challenge: non-deterministic behavior makes a successful demonstration insufficient evidence of production reliability. Prepare an example of how evaluations, monitoring and guardrails would work together, including what happens when an agent fails.
Reusing patterns without forcing a single solution
Likely preparation challenge: reference architectures must transfer across engagements, while technology choices and security constraints may differ by use case. Prepare to explain which components you would standardize and which you would leave adaptable.

Practice your answers

Technical practice

How would you decide between a single-agent and a multi-agent design for a public service workflow?

What to coverSuggested preparation: Compare task boundaries, coordination overhead, failure modes and the need for human review. Explain how you would test whether additional agents improve outcomes enough to justify their complexity.

How would you design tool use and function calling for an agent that takes multiple steps?

What to coverSuggested preparation: Describe how you would define tool permissions, validate inputs and outputs, and handle failed or unexpected calls. Explain how the agent's intermediate actions would remain inspectable before any consequential step.

How would you evaluate a non-deterministic agent before and after deployment?

What to coverSuggested preparation: Discuss representative test cases, measurable task outcomes and safety failures, including how you would review results that vary between runs. Distinguish pre-deployment evaluation from ongoing production checks.

What observability would help you diagnose an unreliable production agent?

What to coverSuggested preparation: Identify the steps and tool interactions you would need to trace to locate a failure. Explain how you would use those observations to distinguish a faulty integration from an agent decision or an evaluation gap.

How would you design guardrails for an agent used in a regulated public sector setting?

What to coverSuggested preparation: Explain where controls should sit around inputs, tool actions and outputs, and how you would test whether they work. Address the trade-off between preventing unsafe behaviour and allowing the agent to complete legitimate tasks.

How would you set autonomy levels, human-in-the-loop checkpoints and escalation rules?

What to coverSuggested preparation: Describe criteria for allowing an agent to act, requiring approval or handing a case to a person. Explain how you would make those decisions reviewable and adjust them when evaluation reveals failures.

What would you include in a reusable reference architecture for agentic delivery?

What to coverSuggested preparation: Separate components that could be shared across engagements from client-specific integrations and governance decisions. Explain how reusable patterns could support delivery teams without assuming identical public sector, defense and public safety requirements.

How would you choose among agent harnesses, IDE-based agents, cloud agents and orchestration frameworks?

What to coverSuggested preparation: Compare each option against the use case's integration needs, operational visibility, guardrails and maintainability. Explain how you would validate a choice with a limited implementation rather than selecting a tool by category alone.

When would retrieval-augmented generation and a vector database help an agentic system?

What to coverSuggested preparation: Treat these as preferred, not required, experience in the posting. Discuss when retrieval could supply task-relevant context and how you would evaluate retrieval quality, access controls and the effect of poor results on agent behaviour.

How would you take an agentic prototype into a production software system?

What to coverSuggested preparation: Discuss integration, testing, deployment and operational ownership in the context of the posting's production-delivery requirement. Explain how you would choose among the listed languages or frontend technologies according to the system's needs, rather than assuming a prescribed stack.

Behavioural practice

Tell me about a time you worked independently through an ambiguous client-facing problem.

What to coverSuggested preparation: Use a real situation to explain what was unclear, how you established priorities with stakeholders and what you did next. Close with the result and what you learned from any remaining uncertainty.

Tell me about a time you aligned stakeholders on a safety or governance trade-off.

What to coverSuggested preparation: Describe the situation, the differing concerns and the actions you took to make a decision understandable. State the result without implying you have worked in a regulated environment if you have not.

Tell me about a time you helped another delivery team adopt an engineering practice.

What to coverSuggested preparation: Explain the team's starting point, how you coached or supported them and the result. Focus on evidence of adoption or changed delivery practices rather than a general claim of influence.

Tell me about a time you turned a one-off solution into something others could reuse.

What to coverSuggested preparation: Identify the original need, what you standardised and what you left configurable. Describe the result, including any limits on reuse that became apparent.

Tell me about a production issue you had to explain to both technical and non-technical stakeholders.

What to coverSuggested preparation: Set out the situation, your troubleshooting and communication actions, and the outcome. Explain how you conveyed uncertainty and follow-up work without overstating what had been resolved.

Sharpen your knowledge

Topics to prepare

Agent autonomy and orchestration
Required: the role covers single- and multi-agent systems, tool use, function calling and multi-step reasoning. Sketch when a public-service workflow needs one agent versus several, and mark where tools are invoked and control returns.
Evaluation and guardrail design
Required: production agent behavior must be measurable, safe and reliable despite non-determinism. Draft an evaluation plan for an illustrative service workflow, including failure cases, success measures and guardrail checks.
Human oversight and escalation
Explicit responsibility: establish autonomy levels, human-in-the-loop checkpoints and escalation protocols in regulated environments. Map a hypothetical workflow into actions an agent may take, actions needing approval and cases requiring escalation.
Reusable delivery architecture
Explicit responsibility: develop reference architectures, tools and patterns that work across engagements. Draw a reference architecture and label what could be reused versus what must change for each client.
Technology selection for public-sector contexts
Explicit responsibility: select and integrate agent harnesses, IDE-based agents, cloud agents or orchestration frameworks to fit a use case. Preferred areas include RAG, vector databases and familiarity with public-sector security policies. Compare options against integration, oversight and security needs without assuming a particular product is required.

Skills to revise

Build an agent workflow
Required hands-on capability: implement a small single-agent workflow with a tool call and a multi-step task in one of the listed languages you use proficiently (Python, Java, React and/or TypeScript). As a preparation exercise, explain how you would extend or constrain it for multiple agents.
Test and observe agent behavior
Required experience includes evaluation and guardrail mechanisms; the role also calls for observability. Create a small set of repeatable test cases, capture tool actions and outcomes, and show how you would spot failures and assess a guardrail.
Design an approval checkpoint
Explicit responsibility: human-in-the-loop governance and escalation. Add an approval step to a sample workflow; demonstrate what happens when approval is granted, denied or unavailable.
Explain and hand off a reusable pattern
Explicit responsibilities include working with clients and delivery teams and coaching agentic engineering practices. Practice a short walkthrough of your sample architecture that explains its trade-offs, reusable parts and client-specific decisions to a delivery team.

Your preparation checklist

Select a production-system example
Prepare a one-page account of a system you delivered, your engineering decisions, deployment responsibilities and outcomes. The posting asks for a strong production-delivery record and proficiency in Python, Java, React and/or TypeScript.
Build an agent-system walkthrough
Prepare a diagram and short demo or code excerpt showing LLM tool use, function calling, multi-step reasoning and orchestration. Explain your choices and failure handling; distinguish work you implemented from a proposed example.
Practice a public-service design exercise
Sketch a hypothetical single- or multi-agent workflow for a public-service task. Show its architecture, integration points and how you would choose among agent harnesses, cloud agents and orchestration frameworks; label assumptions rather than presenting them as Accenture requirements.
Prepare an evaluation and observability plan
Draft example test cases, measures, traces and guardrails for a non-deterministic agent. Include how you would detect unsafe or unreliable behavior after deployment.
Define autonomy and escalation boundaries
Write a decision table for a hypothetical regulated workflow showing permitted agent actions, human approval checkpoints and escalation triggers. Be ready to explain the trade-offs to a client stakeholder.
Show how a solution could be reused
Prepare a reference-architecture outline or reusable tool pattern, plus a brief explanation of how you would document it and coach a delivery team to adopt it across engagements.
Bring targeted employer questions
Write questions about the team's current agent use cases, evaluation expectations, autonomy governance, public-sector security constraints and how reusable capabilities are shared across delivery teams.

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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