Senior Manager - AI-Driven Software Development Life Cycle Strategy

S&C GN - TS&T – Digital Foundations - AI SDLC - Senior Manager

Technology Strategy and Transformation – Senior Manager – AI driven SDLC strategy

Join our team in Technology Strategy & Transformation for an exciting career opportunity to help our most strategic clients realize exceptional value from AI in software delivery, a boardroom priority for organizations globally and be at the forefront of shaping how enterprises adopt AI-native software engineering

Practice: Technology Strategy & Transformation, Global Network
Areas of Work: AI Native SDLC Strategy, Agentic AI Architecture, Agentic Software Engineering, Engineering Productivity, AI Platform & Tooling Strategy
Level: Senior Manager
Location: Bangalore/Gurugram/Mumbai/Pune/Chennai/Kolkata/Hyderabad
Years of Exp: 15+ years

Explore an Exciting Career at Accenture

Do you believe software engineering is entering a new AI-native era? Are you a problem solver who enjoys helping CIOs, CTOs, CDOs and engineering leaders rethink how software is imagined, designed, built, tested, secured and delivered using AI? Are you passionate about being part of an inclusive, diverse and collaborative culture?

If yes, this is the right opportunity for you. Join Accenture Technology Strategy & Transformation practice and work with global clients to shape the next frontier of enterprise software delivery using AI. Working with C-suite stakeholders, you will help enterprises adopt AI-native software delivery at scale and turn transformation ambition into measurable outcomes. You will help enterprises define AI-powered software delivery transformation strategies and translate them into executable approaches, define target state SDLC, create platform and tooling strategy and architecture, engineering productivity measures, delivery operating model change and measurable value realization.

The Practice – A Brief Sketch

Technology Strategy & Transformation Practice is a part of Accenture Strategy and focuses on our clients’ most strategic priorities. We help clients achieve growth and efficiency through engineering transformation initiatives, aimed at making software delivery powered by AI to bring productivity and quality uplift, resulting in larger enterprise value delivery.

We provide you with a strong learning environment, deep-rooted in Technology Strategy, Software Engineering Transformation and AI-led Reinvention, where you will work with key global clients to shape the next evolution of enterprise software delivery. As part of this high-performing team, you will help organizations move from experimentation with AI coding tools to enterprise-wide AI Native SDLC transformation. These are some of the initiatives you will support:

AI Native SDLC Strategy and Roadmap: Assess current SDLC maturity, AI readiness, engineering productivity, application landscape assessment for AI driven delivery, toolchain landscape and delivery bottlenecks; define target-state AI Native SDLC vision and pragmatic adoption roadmap Engineering Productivity and AI Readiness Diagnosis: Diagnose developer experience, flow of work, quality gates, release throughput, automation levels, technology debt, test coverage, knowledge fragmentation and value leakage across large engineering organizations Use Case Identification and Prioritization: Identify and prioritize AI and agentic use cases across requirements, backlog, architecture, design, coding, refactoring, testing, code review, security, release, documentation and run/operate interfaces; segment applications and teams where adoption makes business and technology sense AI Native SDLC Platform and Tooling Strategy & Architecture: Define architecture principles, reference architecture and tooling strategy across IDEs, repositories, CI/CD, DevSecOps, testing, knowledge systems, model gateways, context layer, RAG, agent orchestration, MCP, observability and governance Value Case and Benefits Framework: Define productivity, velocity, quality, cost, risk and developer experience outcomes; establish measurement model, value case, benefits tracking and AI cost governance Delivery Operating Model and Change: Design target-state engineering operating model considering AI native software delivery and help organization transition to new operating model Executive Advisory and Scale Strategy: Shape CxO narratives, transformation roadmaps, investment choices, vendor strategy and scale-up plan for enterprise-wide AI Native SDLC adoption

Role Overview​

We are looking for a visionary leader in Technology Strategy and Software Engineering Transformation who can help enterprises redefine how software is delivered in an AI-native world. The role requires a senior consulting practitioner with strong understanding of enterprise SDLC, AI/ GenAI and Agentic architecture, engineering productivity, Agile, CI/CD and DevSecOps.

The successful candidate will lead strategic advisory work across the AI Native SDLC transformation journey from current-state maturity and AI readiness assessment to target-state SDLC design, AI use case prioritization, platform and tooling strategy, value case development, benefits framework, roadmap definition and operating model change.

The role requires executive presence, strong consulting capability and the ability to independently engage CIOs, CTOs, CDOs, CISOs, engineering leaders and product/platform teams. The candidate should be able to shape compelling transformation narratives, facilitate senior stakeholder alignment and translate AI-powered software delivery ambition into practical, business-aligned decisions and measurable outcomes.

Key Responsibilities​

​AI Native SDLC Strategy and Advisory

• Lead current-state SDLC maturity, AI readiness and engineering productivity assessments across large engineering organizations
• Define target-state AI Native SDLC ambition and strategy across all stages of fotware delivery and software maintenance
• Conduct application landscape assessment to Identify applications, products and teams best suited for AI-native and agentic SDLC adoption based on value, feasibility, risk and readiness
• Identify, structure and prioritize AI and agentic use cases across the software delivery lifecycle, linking them to engineering productivity, quality and measurable outcomes
• Facilitate senior stakeholder alignment on the adoption priorities, transformation choices and approach

Target-State SDLC Design, Architecture and Tooling Strategy

• Design AI-Native delivery workflows and agentic patterns across all stages of SDLC
• Define AI Native SDLC platform reference architecture across ALM, IDEs, repositories, CI/CD, DevSecOps, testing, observability, knowledge systems, model gateways, RAG/context layer, MCP/tool interfaces and guardrails with responsible AI frameworks pre-built in the design
• Advise clients on AI platform, toolchain and vendor strategy, including integration approach, security, data/IP considerations, cost and developer adoption
• Define architecture principles, reusable patterns, guardrails, governance constructs and agentic evaluation criteria required to scale AI-Native engineering responsibly
• Work with architecture, platform, security and engineering teams to connect strategy with implementation realities while maintaining an advisory and transformation-planning focus

Value Realization, Productivity Measurement and Engineering Operating Model

• Build AI Native SDLC value cases covering various dimensions across productivity, quality, risk, experience and cost
• Define benefits framework and productivity measurement approach using baselines, KPIs, telemetry, DORA, SPACE, flow metrics, adoption metrics and executive reporting
• Define AI value economics and cost governance across licensing, token consumption, model usage, chargeback/showback, cost guardrails and benefit realization
• Design target engineering operating model covering product/platform ownership, AI SDLC center of excellence, roles and responsibilities, governance forums, enablement and change adoption
• Translate target-state architecture and operating model into a phased transformation roadmap covering quick wins, foundational enablers, pilots, scaling waves and governance milestones

Executive Advisory and Market Development

• Lead CxO-level conversations on AI Native SDLC strategy, engineering productivity, platform choices, tooling strategy, value realization, governance and operating model change
• Create expert content, PoVs and use executive storytelling, presentation and communication skills for C-level discussions
• Support strategic pursuits, client account development and growth initiatives through differentiated advisory propositions, points of view, solution narratives and proposal leadership.
• Collaborate across different capabilities and teams to shape holistic client solutions and connect AI Native SDLC transformation with broader enterprise reinvention priorities
• Develop thought leadership, market perspectives, offering assets and reusable modernization frameworks
• Lead and mentor teams on AI, modern engineering, software delivery and engineering productivity measures

Core Skillsets & Tools​

Enterprise Software Engineering and SDLC

• Strong understanding of enterprise software delivery, including Agile, DevOps, DevSecOps, CI/CD, test automation, secure SDLC, release management, platform engineering and SRE concepts.
• Ability to assess SDLC maturity, engineering practices, delivery bottlenecks, toolchains, governance and productivity across large, distributed engineering organizations.
• Strong understanding of engineering productivity, developer experience, software quality, technical debt, application complexity and delivery value streams.
• Ability to design future-state SDLC processes, governance models and transformation roadmaps aligned to business and engineering outcomes.
• Application Architecture, Engineering and AI

AI / Agentic Fluency, Tools and Platform Capabilities

• Strong understanding of GenAI, LLMs and agentic software engineering concepts, including prompt engineering, context engineering, RAG, MCP, agent orchestration, MCP, guardrails and evaluations
• Practical understanding of AI-assisted SDLC use cases across requirements, backlog, architecture, coding, code review, refactoring, testing, security, documentation, release support and operations handover
• Familiarity with leading AI coding and SDLC tools such as GitHub Copilot, OpenAI Codex, Claude Code, Cursor, Windsurf, GitLab Duo, Amazon Q Developer, Gemini Code Assist, Sourcegraph Cody, Atlassian Rovo or equivalent tools
• Familiarity with AI platforms and agentic orchestration tools such as Microsoft/Azure AI Foundry, Amazon Bedrock, Google Vertex AI, LangGraph, LlamaIndex, Semantic Kernel, AutoGen/CrewAI, LangSmith/MLflow or equivalent platforms.
• Understanding of enterprise context and knowledge architectures using code indexing, embeddings, vector databases, enterprise search, knowledge graphs, RAG and model ecosystems including OpenAI, Anthropic, Gemini, Llama and open-source models.
• Ability to define toolchain architecture, vendor evaluation criteria, integration patterns, pilot approach, rollout considerations and governance required for scalable AI Native SDLC adoption.

Value, Economics and Productivity Measurement

• Ability to formulate AI Native SDLC value cases across productivity, cycle time, quality, risk, cost, developer experience and business agility outcomes
• Strong understanding of productivity measurement approaches including DORA, SPACE, flow metrics, adoption telemetry, developer surveys, quality metrics and release metrics
• Ability to assess AI economics, including license cost, token consumption, model usage, cost-to-serve, utilization, chargeback/showback and benefit realization governance
• Ability to connect AI adoption choices to measurable business outcomes and executive-level investment decisions

Consulting and Communication Skills

• Strong consulting toolkit across structured problem solving, hypothesis-led analysis, benchmarking, assessment design, facilitation, executive storytelling, business case development and roadmap definition
• Ability to drive CxO-level conversations with credibility, influence senior stakeholders and align business, engineering, architecture, security, finance and technology teams
• Ability to independently lead client discussions, develop thought leadership, create compelling transformation narratives and shape opportunities in ambiguous environments
• Experience leading proposals, strategic pursuits, senior client relationships and high-performing consulting teams

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