Highlights:
10.00 – 15.00 Years
25.00 – 40.00 INR (Lacs)/Yearly
Full-time
Delhi, Gurgaon, Bengaluru
Skills
SDLC
Roles & Responsibility
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
Requirements
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: 10-15 years
