Senior Solution Architect

Job description

The AI Native AD Solution Architect / LSA helps customers adopt the AI-Led software development lifecycle and turns that advice into winning, deliverable solutions. The role leads discovery, runs AI-Led SDLC maturity assessments, and coaches customers on embracing AI-led ways of working. It leads RFP/RFI solutioning end to end: target architecture, estimation assumptions, delivery model, NFRs, risks, proposition composition, and technical defence. It then carries the solution through mobilization and transition. It shapes both greenfield AI-native solutions and brownfield modernization programs. The role is a hands-on, AI-native engineer-architect and a credible customer-facing advisor, able to talk to CXOs and to engineering teams. Lead discovery, AI-Led SDLC maturity assessment, coach customers on embracing the AI-Led SDLC, Advisory, RFP/RFI solutioning, target architecture, estimation assumptions, delivery model, NFRs, risks, proposition composition, technical defence, mobilization and transition; shape greenfield AI-native and brownfield modernization solutions. Practical AI-native engineering; strong modern application development; cloud-native, API, microservices and DevSecOps grounding; hands-on experience with at least one agent harness such as Claude Code, GitHub Copilot, Codex, Kiro or Cursor; enterprise security, governance, evaluation, observability and cost awareness. Key Responsibilities A. Discovery & Advisory Lead discovery with customer business and technology stakeholders: goals, constraints, current SDLC, toolchain, team structure, applications landscape, and delivery pain points. Provide advisory on AI-Led SDLC adoption: where AI fits across requirements, design, coding, testing, security, release, and operations, and what changes in roles, practices, and governance. Build the business case and value hypothesis (productivity, quality, speed, cost, risk) for AI-Led SDLC adoption. B. AI-Led SDLC Maturity Assessment & Coaching Run AI-Led SDLC maturity assessments across people, process, tooling, governance, security, measurement, and culture; baseline current state and define target state. Produce findings, gap analysis, and a phased adoption roadmap with quick wins, pilots, and scale-up stages. Coach customers on embracing the AI-Led SDLC: agent harness adoption, prompt and context practices, spec-driven and review-driven workflows, human-in-the-loop controls, metrics, and change management. Define adoption metrics and outcome measures (e.g., cycle time, throughput, defect rates, review effort, developer experience, AI cost per change). C. RFP/RFI Solutioning Lead RFP/RFI response solutioning: requirements analysis, win themes, solution approach, and differentiation. Define target architecture for the proposed solution, covering application, data, AI, integration, security, and infrastructure layers. Define NFRs (performance, scalability, availability, resilience, security, compliance, observability, cost) and how the solution meets them. Define estimation assumptions , sizing approach, and AI-productivity assumptions with explicit, defensible rationale. Define the delivery model : team structure, pods, onshore/offshore mix, AI-augmented ways of working, governance cadence, and tooling. Identify and quantify risks , dependencies, and mitigations; shape commercial constructs with the pursuit team. Compose the proposition : narrative, architecture views, case studies, accelerators, and value story. D. Solution Shaping: Greenfield & Brownfield Shape greenfield AI-native solutions: AI-first architecture, agentic components, evaluation and observability by design, security and governance built in. Shape brownfield modernization solutions: assessment of legacy estate, modernization strategy (rehost, replatform, refactor, rearchitect, replace), AI-assisted code understanding and migration, and phased coexistence. Apply the AI-Led SDLC to both: what is automated, what remains human-led, and how quality and risk are controlled. E. Technical Defence Lead technical defence of the proposed solution in customer presentations, orals, and due diligence sessions. Prepare for and handle deep technical challenge on architecture, AI approach, security, delivery model, and estimates. Run demos and PoCs that demonstrate the AI-Led SDLC in practice where needed to win trust. F. Mobilization & Transition Lead mobilization of won engagements: solution handover to delivery, team ramp-up, tooling and environment setup, ways-of-working onboarding, and governance setup. Lead transition planning (knowledge transfer, steady-state handover, or transformation from the customer's current model), with clear exit criteria. Ensure the delivery team has the reference architectures, accelerators, and AI-Led SDLC playbooks needed to deliver what was sold. G. Governance, Reuse & Enablement Embed enterprise security, governance, evaluation, observability, and cost controls into every proposed solution and delivery model. Contribute reusable assets back to CoE and Advisory: assessment frameworks, proposal templates, estimation models, case studies, and reference architectures. Mentor solution architects and consultants; collaborate with CoE, Solutions, and delivery leadership. Skill Requirements Bachelor's or Master's degree in Computer Science, Engineering, or an equivalent field, or equivalent practical experience. 14+ years in software engineering, architecture, and delivery, with at least 5 years in customer-facing solutioning, pre-sales, or advisory roles and at least 2 years working with AI-assisted or AI-native development. Track record of leading RFP/RFI responses, technical defence, and handover into delivery for large engagements. Technical & Advisory Skills (Must Have) AI-Led SDLC knowledge: how AI changes each SDLC phase, agent harness usage, spec-driven and agentic development workflows, human-in-the-loop controls, and productivity measurement. Maturity assessment and advisory: assessment frameworks, gap analysis, roadmap creation, and coaching customers through change. Discovery and consulting skills: structured discovery, stakeholder management, value hypothesis and business case building. Solution architecture: target architecture across application, data, AI, integration, security, and infrastructure for greenfield and brownfield. NFR definition: performance, resilience, security, compliance, observability, and cost NFRs and how to engineer for them. Estimation and delivery model design: sizing methods, assumptions, AI productivity factors, team structure, governance, and commercial construct awareness. Risk management: identifying, quantifying, and mitigating technical, delivery, and commercial risks. Proposal craft and technical defence: proposition composition, win themes, presenting and defending solutions under challenge. Mobilization and transition: onboarding, knowledge transfer, handover, and ramp-up planning. Modernization: legacy assessment, modernization strategies, and AI-assisted code understanding and migration. AI-native engineering: practical use of AI across the development lifecycle; hands-on with at least one agent harness (Claude Code, GitHub Copilot, Codex, Kiro, or Cursor). Modern application development: strong proficiency in at least one language such as Java, Python, TypeScript, or C#; clean architecture, testing, code review. Cloud-native, API & microservices: AWS/Azure/GCP, containers, service design, event-driven architecture. DevSecOps: CI/CD, infrastructure as code, secure SDLC, quality and security gates. Enterprise readiness: security, governance, evaluation, observability, and cost awareness, including cost modeling of AI tooling and delivery. Behavioral & Professional Skills Executive presence and storytelling; explains AI-Led SDLC value and risk clearly to CXOs and to engineers. Consultative, curious, and commercially aware; balances ambition with delivery realism. Credible under challenge; honest about limits of AI prod