Job Summary

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)

1. 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.
2. Maturity assessment and advisory: assessment frameworks, gap analysis, roadmap creation, and coaching customers through change.
3. Discovery and consulting skills: structured discovery, stakeholder management, value hypothesis and business case building.
4. Solution architecture: target architecture across application, data, AI, integration, security, and infrastructure for greenfield and brownfield.
5. NFR definition: performance, resilience, security, compliance, observability, and cost NFRs and how to engineer for them.
6. Estimation and delivery model design: sizing methods, assumptions, AI productivity factors, team structure, governance, and commercial construct awareness.
7. Risk management: identifying, quantifying, and mitigating technical, delivery, and commercial risks.
8. Proposal craft and technical defence: proposition composition, win themes, presenting and defending solutions under challenge.
9. Mobilization and transition: onboarding, knowledge transfer, handover, and ramp-up planning.
10. Modernization: legacy assessment, modernization strategies, and AI-assisted code understanding and migration.
11. 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).
12. Modern application development: strong proficiency in at least one language such as Java, Python, TypeScript, or C#; clean architecture, testing, code review.
13. Cloud-native, API & microservices: AWS/Azure/GCP, containers, service design, event-driven architecture.
14. DevSecOps: CI/CD, infrastructure as code, secure SDLC, quality and security gates.
15. 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 productivity claims and able to back estimates with evidence.
• Coaching mindset, leadership, and collaboration across Advisory, Solutions, CoE, and delivery teams.

Other Requirements

Role-specific

• Developer productivity and engineering metrics frameworks (DORA, SPACE, developer experience surveys).
• Change management and organizational design for AI-augmented engineering teams.
• Commercial constructs: outcome-based pricing, managed-service and productivity-linked models.
• Application portfolio assessment and rationalization; cloud migration and mainframe/legacy modernization experience.
• Agent frameworks and agentic application patterns; MCP and tool integration.
• Quality engineering and test automation strategy in AI-led delivery.
• Responsible AI governance, IP and licensing considerations for AI-generated code.
• Industry domain expertise in BFSI, healthcare, telecom, retail, manufacturing, or public sector.
• Thought leadership: publishing, speaking, and analyst or client briefings.

Common competencies (mandatory across all roles)

• Practical AI-native engineering and strong modern application development.
• Cloud-native, API, microservices, and DevSecOps grounding.
• Hands-on experience with at least one agent harness (Claude Code, GitHub Copilot, Codex, Kiro, Cursor).
• Enterprise security, governance, evaluation, observability, and cost awareness.

Certifications (preferred, not mandatory)

• Cloud solution architect certifications (AWS, Azure, or GCP).
• TOGAF or equivalent architecture certification.
• Delivery and agile certifications (e.g., SAFe, PMP, or equivalent) and relevant AI or GitHub/Microsoft developer-tool certifications.