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

Job description

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 phase