The Senior AI Architect and Team Lead will provide architectural leadership, AI thought leadership, and delivery enablement for complex legacy modernization and migration initiatives. The role is intended to help teams move from legacy technology stacks to modern, AI-enabled target architectures in a structured, secure, scalable, and business-aligned manner. In parallel, the role will provide day-to-day leadership for the Fusion Team in DTDL India, ensuring prioritization, team alignment, execution discipline, and effective collaboration with related AI and technology teams.
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AI and legacy migration architecture: Act as senior architect and thought leader for AI-supported migration and modernization of large and extra-large use cases.
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AI and legacy migration architecture: Act as senior architect and thought leader for AI-supported migration and modernization of large and extra-large use cases.
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Team leadership: Lead the Fusion Team in DTDL India, including team cohesion, prioritization, planning, and delivery coordination.
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Cross-team enablement: Optionally act as a connecting role between Tech Enablemers AI, DTDL India (e.g. AskT, AI DLC, and other related product, platform, and architecture teams)
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Operating model contribution: Define reusable practices, decision frameworks, architecture patterns, and governance mechanisms for repeatable modernization delivery.
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3. Responsibilities and Activities
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Provide senior architectural guidance for AI-enabled modernization, migration, refactoring, and re-platforming initiatives across large and extra-large use cases.
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Provide senior architectural guidance for AI-enabled modernization, migration, refactoring, and re-platforming initiatives across large and extra-large use cases.
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Translate business objectives and product needs into clear architecture principles, solution blueprints, migration hypotheses, and technical decision records.
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Ask and structure the critical discovery questions needed for successful migration, including current legacy technology stack, target technology stack, dependencies, integration patterns, data flows, security constraints, performance requirements, team capabilities, and business-critical risks.
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Evaluate how AI, automation, code analysis, knowledge graphs, code transformation tooling, or human-in-the-loop review can accelerate understanding, documentation, migration planning, testing, and refactoring.
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Define target-state architecture options and migration paths, including incremental migration, modularization, API enablement, cloud-native patterns, DevSecOps, observability, and fallback strategies.
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Establish architectural guardrails for scalability, maintainability, reliability, security, compliance, cost transparency, responsible AI, and operational resilience.
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Guide teams in selecting appropriate modernization strategies, for example retain, retire, rehost, re-platform, refactor, replace, or re-architect, based on business value, risk, technical debt, and long-term strategic fit.
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Support solution reviews, design critiques, risk assessments, and technical trade-off decisions for migration and AI implementation workstreams.
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Contribute to reusable templates, assessment checklists, architecture decision records, patterns, and playbooks for repeatable migration enablement.
- 4. Required Skills and Experience
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Senior-level architecture experience in complex enterprise environments, ideally including modernization of legacy applications and large-scale software platforms.
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Senior-level architecture experience in complex enterprise environments, ideally including modernization of legacy applications and large-scale software platforms.
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Strong understanding of AI-enabled engineering practices, AI solution architecture, MLOps or LLMOps concepts, data and integration architecture, and responsible AI principles.
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Experience with migration strategy, codebase assessment, refactoring, cloud-native architecture, API design, DevSecOps, observability, and secure-by-design delivery.
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Ability to lead technical discovery and ask the right diagnostic questions across legacy stack, target stack, dependencies, data, security, testing, operations, and delivery constraints.
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Proven leadership capability in prioritizing work, coordinating teams, coaching engineers, and communicating clearly with senior stakeholders.
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Strong facilitation, documentation, decision-making, and stakeholder management skills.
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Ability to operate in ambiguity and convert complex technical topics into practical delivery plans.
