New opportunity

AI SDLC – Senior Manager

Accenture · Gurugram, India

About this role

Accenture seeks a Senior Manager in Technology Strategy & Transformation to advise global clients on enterprise-wide AI-native software delivery. The role focuses on AI-driven SDLC strategy, agentic software engineering, platform and tooling architecture, engineering productivity, and measurable transformation outcomes. The listed locations are Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad; the job header identifies Gurugram.

Responsibilities

Lead assessments of SDLC maturity, AI readiness, engineering productivity, application landscapes, toolchains and delivery bottlenecks. Define target-state AI-native SDLC strategies and phased adoption roadmaps.
Identify and prioritize AI and agentic use cases across requirements, design, coding, refactoring, testing, code review, security, release, documentation and operations, considering value, feasibility, risk and readiness.
Design AI-native workflows and reference architectures spanning IDEs, repositories, CI/CD, DevSecOps, testing, observability, knowledge systems, model gateways, RAG and context layers, MCP interfaces, governance and responsible-AI guardrails. Advise on tool and vendor selection, integration, security, data and intellectual-property considerations, and adoption.
Develop value cases and benefits frameworks for productivity, quality, risk, cost and developer experience. Establish baselines and KPIs using DORA, SPACE, flow metrics and adoption telemetry; address licensing, token consumption and model-usage costs.
Design engineering operating models, governance and change plans. Lead CxO discussions, strategic proposals and client pursuits; develop thought leadership and mentor consulting teams.

Requirements and capabilities:

The detailed role section states 15+ years of experience, while the job header lists 12–14 years. The role calls for senior consulting experience, executive communication, stakeholder alignment and a strong understanding of enterprise SDLC, Agile, CI/CD, DevSecOps, GenAI, LLMs and agentic architecture.
Relevant familiarity includes AI coding tools such as GitHub Copilot, OpenAI Codex and Claude Code; AI platforms and orchestration tools such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI and LangGraph; and enterprise knowledge architectures using embeddings, vector databases, search and RAG.

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

Skills for this role

AI-native software development lifecycleAgentic AI architectureAgentic software engineeringEngineering productivityTechnology strategySoftware engineering transformationAgileDevOpsDevSecOpsCI/CDTest automationPlatform engineeringSite reliability engineeringGenerative AILarge language modelsPrompt engineeringContext engineeringRetrieval-augmented generationModel Context ProtocolAgent orchestrationAI evaluationResponsible AIGitHub CopilotOpenAI CodexClaude CodeCursorWindsurfGitLab DuoAmazon Q DeveloperGemini Code Assist

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

Diagnose and sequence AI-native SDLC transformation
The role leads assessments of SDLC maturity, AI readiness, productivity, application landscapes and delivery bottlenecks, then defines a target state and phased roadmap. Preparation suggestion: be ready to explain how assessment findings would determine pilots, foundational work and scaling waves.
Prioritize use cases by enterprise fit
The role identifies AI and agentic opportunities across the delivery lifecycle and selects applications and teams based on value, feasibility, risk and readiness. Preparation suggestion: articulate a decision framework rather than treating every use case as equally suitable.
Shape platform architecture and safeguards
The role defines AI-native workflows and reference architectures spanning development tools, CI/CD, knowledge and context layers, model gateways and agent interfaces. It also advises on integration, security, data/IP, evaluation and responsible-AI guardrails.
Make value and costs measurable
The role builds value cases and benefits tracking across productivity, quality, risk, experience and cost, using baselines, DORA, SPACE, flow metrics and adoption telemetry. It also addresses licensing, token and model-usage costs.
Lead executive decisions and operating-model change
The role aligns CxO and engineering stakeholders on investment, tooling, governance and adoption; designs the target operating model; and leads proposals, thought leadership and consulting teams.

Likely challenges to think through

Choosing where to start without overgeneralizing
Preparation hypothesis: a large client's applications, toolchains and team readiness may vary, making a single rollout plan difficult to justify. Prepare a way to segment candidates, compare value against feasibility and risk, and explain what would trigger a later scaling wave.
Proving gains without confusing activity with outcomes
Preparation hypothesis: higher AI-tool adoption may not translate directly into faster delivery or better quality, while usage costs rise. Prepare a measurement approach with pre-adoption baselines, delivery and quality indicators, developer feedback, and explicit license and model-cost tracking.
Balancing agentic capability with enterprise controls
Preparation hypothesis: more autonomous workflows may improve throughput but increase security, IP, reliability and governance concerns. Prepare to describe bounded agent permissions, evaluation and review gates, and how those controls fit into existing engineering workflows.
Turning executive ambition into workable change
Preparation hypothesis: CxO investment goals may compete with platform, security and engineering constraints. Prepare to show how you would surface trade-offs, assign operating-model ownership and sequence decisions from pilot to enterprise adoption.

Practice your answers

Technical practice

How would you assess an enterprise’s SDLC maturity and AI readiness?

What to coverThe role calls for assessing delivery bottlenecks, toolchains, application landscapes and engineering productivity. Suggested answer: explain how you would combine workflow evidence, quality and release data, developer feedback and governance review to establish a baseline before proposing a target state.

How would you prioritize AI and agentic SDLC use cases?

What to coverThe posting spans requirements through operations and calls for prioritization by value, feasibility, risk and readiness. Suggested answer: describe how you would segment applications and teams, compare candidate use cases and choose pilots with measurable outcomes.

How would you design an AI-native workflow for coding, testing and review?

What to coverThe role includes designing delivery workflows and agentic patterns across the SDLC. Suggested answer: explain where human review, automated tests, security checks and escalation belong, and how those controls affect speed and quality.

What would an enterprise AI-native SDLC reference architecture include?

What to coverThe posting names IDEs, repositories, CI/CD, DevSecOps, testing, observability, knowledge systems, model gateways and governance. Suggested answer: show how these components connect to agent workflows, identify integration boundaries and explain where responsible-AI guardrails apply.

How would you provide reliable enterprise context to an engineering agent?

What to coverThe role names code indexing, search, embeddings, vector databases, RAG, context layers and MCP interfaces. Suggested answer: discuss access control, retrieval relevance and stale knowledge, then explain how you would diagnose an agent response based on incorrect or missing context.

How would you evaluate and govern an agentic software-engineering use case?

What to coverThe posting calls for agentic evaluation criteria, reusable guardrails and responsible-AI governance. Suggested answer: cover task success, code quality, security and failure handling, including when an agent should require human approval rather than act autonomously.

How would you compare AI coding tools and platform vendors for a client?

What to coverThe role includes toolchain and vendor strategy, integration, security, data and intellectual-property considerations, cost and developer adoption. Suggested answer: propose evaluation criteria and a pilot that tests fit with the existing environment rather than assuming one tool suits every team.

How would you measure whether AI adoption improves engineering outcomes?

What to coverThe posting calls for baselines, KPIs, DORA, SPACE, flow metrics, adoption telemetry, developer surveys and executive reporting. Suggested answer: distinguish tool usage from outcomes and explain how you would interpret changes in throughput alongside quality, risk and developer experience.

How would you build an AI SDLC value case and control its costs?

What to coverThe role covers benefits tracking and economics across licensing, token consumption, model usage, utilization and chargeback or showback. Suggested answer: connect costs to expected productivity and quality outcomes, state assumptions and describe how results would inform investment decisions.

How would you turn a target-state design into a phased transformation roadmap?

What to coverThe posting calls for quick wins, foundational enablers, pilots, scaling waves, governance milestones and a changed engineering operating model. Suggested answer: explain sequencing dependencies, product and platform ownership, enablement and the evidence needed before scaling.

Behavioural practice

Tell me about a time you aligned senior stakeholders on a contested technology decision.

What to coverThe role calls for CxO engagement and alignment on transformation priorities. Suggested preparation: use situation, action and result to show how you surfaced competing objectives, framed trade-offs and reached a decision.

Describe a time you diagnosed an ambiguous delivery problem across a large organization.

What to coverThe posting emphasizes structured assessment and hypothesis-led analysis across distributed engineering organizations. Suggested preparation: use situation, action and result to explain the evidence you gathered, how your diagnosis changed and what decision it enabled.

Give an example of reconciling engineering, architecture and security concerns.

What to coverThe role involves working across these groups to connect AI SDLC strategy with implementation realities. Suggested preparation: use situation, action and result to explain the disagreement, the trade-off you facilitated and the resulting approach.

Tell me about a strategic proposal or client pursuit you led.

What to coverThe posting includes proposal leadership, differentiated advisory propositions and executive storytelling. Suggested preparation: use situation, action and result to show how you shaped the narrative, coordinated contributors and assessed the outcome without overstating your contribution.

Describe a time you mentored a team through a change in engineering practice.

What to coverThe role includes mentoring consulting teams and planning operating-model change and adoption. Suggested preparation: use situation, action and result to show how you developed others, addressed resistance and observed whether the change took hold.

Sharpen your knowledge

Topics to prepare

Enterprise SDLC and engineering productivity
The role calls for understanding Agile, CI/CD, DevSecOps, test automation, release management and delivery bottlenecks. Suggested revision: trace a hypothetical change from backlog to production and explain where flow, quality or developer experience could deteriorate.
AI-native and agentic delivery patterns
The posting covers AI use cases across requirements, coding, testing, security, release and operations. Suggested revision: compare an AI-assisted coding workflow with an agentic workflow, explaining where human review and evaluation belong.
Platform architecture and responsible guardrails
The role specifies IDEs, repositories, CI/CD, model gateways, RAG and context layers, MCP interfaces, observability and governance. Suggested revision: explain how context access, data/IP protection, security controls and agent evaluation affect an enterprise architecture.
Value measurement and AI economics
The posting names DORA, SPACE, flow metrics, adoption telemetry, quality measures and licensing and token costs. Suggested revision: explain how to establish a baseline and distinguish measured delivery improvement from tool adoption alone.
Operating model and transformation sequencing
The role includes product and platform ownership, governance, enablement and phased rollout. Suggested revision: compare what must be decided for a pilot with what must be in place before enterprise-wide scaling.

Skills to revise

Conduct an SDLC readiness assessment
Practice producing a one-page assessment for a hypothetical engineering organization: evidence to request, maturity findings, bottlenecks and AI-readiness risks. This mirrors the role’s current-state advisory work.
Prioritize AI use cases
Practice scoring three stated SDLC use cases, such as code review, testing and documentation, against value, feasibility, risk and readiness; recommend a pilot and justify exclusions. The posting explicitly requires use-case prioritization.
Sketch a reference architecture
Practice drawing an advisory-level workflow connecting a repository, CI/CD, knowledge or RAG layer, model gateway, MCP/tool interface and security guardrails. Annotate integration and data/IP decisions; architecture and tooling strategy are core responsibilities.
Build a benefits and cost framework
Practice making a small measurement table with baseline, target, data source and review cadence for flow, quality, adoption and developer experience; add licensing and token-usage assumptions. The role requires value cases, benefits tracking and AI cost governance.
Present an executive transformation recommendation
Practice a brief CxO-facing narrative that links assessment findings to investment choices, a phased roadmap, governance milestones and measurable outcomes. This reflects the posting’s executive advisory and stakeholder-alignment responsibilities.

Your preparation checklist

Map evidence to the role
Prepare a one-page index of examples covering enterprise SDLC, Agile, CI/CD, DevSecOps, GenAI and agentic architecture, executive advisory, proposals and team leadership. The posting identifies these as core capabilities.
Rehearse a transformation walkthrough
Prepare a concise, anonymized project story showing the starting SDLC constraints, assessment method, stakeholder decisions, roadmap and measured outcomes. If an exact AI-native example is unavailable, distinguish comparable experience from a proposed approach.
Practice an AI-readiness assessment
Draft a sample assessment covering application landscapes, toolchains, developer experience, quality gates, test coverage and delivery bottlenecks. Show what evidence you would collect before recommending adoption.
Prioritize SDLC use cases
Create a small scoring matrix for AI or agentic use cases across stages such as requirements, coding, testing and security. Explain rankings using the role’s stated criteria: value, feasibility, risk and readiness.
Sketch a target-state architecture
Prepare a reference diagram connecting IDEs, repositories, CI/CD, testing, knowledge and RAG layers, model gateways, agent orchestration and MCP interfaces. Annotate integration, data/IP security, evaluation and responsible-AI decisions.
Build a measurable value case
Draft a sample baseline and benefits scorecard using appropriate DORA, SPACE, flow, quality and adoption measures. Include assumptions for licensing, token and model-usage costs, and explain how results would be tracked.
Present a phased executive roadmap
Prepare a short CxO-style narrative linking priorities and investment choices to pilots, foundational enablers, scaling waves and governance milestones. Include proposed product/platform ownership, change adoption and decision forums.
Prepare questions for the employer
Ask how this team divides advisory work from implementation, how it evaluates pilot outcomes and agentic guardrails, and what senior managers own in pursuits and team mentoring. Clarify the posting’s differing experience figures: 12–14 years in the header and 15+ years in the role section.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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