What You Will Own

● Program scale: Modernization programs spanning dozens of applications across multiple overlapping delivery phases, driven by hard client deadlines such as platform decommissioning or infrastructure exit dates.

● AI-native delivery model: An AI Migration Factory approach — purpose-built AI skills spanning Discovery (feature extraction, business rules intelligence, dependency mapping), Migration (solution design, code generation), and Testing (behaviour-driven test design, automated test generation, quality intelligence) — with human-in-the-loop engineering review at every stage.

● Commercial model: Phase-gated milestone billing (Architecture Sign-off, UAT-Ready, Production Deployment, Knowledge Transfer) with early-phase velocity actuals calibrating pricing for subsequent phases under contractual velocity bands.

● Quality commitments: High automated test coverage targets on core modules, zero critical/major defects at UAT gates, performance parity with legacy baselines, and full knowledge transfer enabling client self-sufficiency.

Key Responsibilities

Program Delivery & Milestone Leadership

● Own end-to-end delivery accountability across all program phases — from Discovery sign-off through Production Deployment and Knowledge Transfer — against contractual milestone gates per application.

● Run programs on a comprehensive delivery metrics framework spanning Velocity, Quality & Parity, Risk & Scope, Governance, AI Effectiveness, Post-Go-Live Stability, User Adoption, and Code Quality — with weekly scorecard reporting and defined escalation triggers.

● Manage phase overlap execution — multiple phases running simultaneously across delivery pods — ensuring velocity calibration from early-phase actuals holds across subsequent phase pricing and planning.

● Enforce Discovery discipline: business rule catalog completeness, SME validation gates, gap analysis reconciliation,



and formal client sign-off before execution code begins.

● Own UAT and acceptance management including acceptance windows, defect cure obligations, hypercare stabilization, and scope boundary enforcement through Change Order governance.

AI-Native Delivery Leadership

● Champion and govern AI-assisted delivery toolkits — monitor AI code generation acceptance rates, business rule extraction recall, and AI-driven test coverage contribution, triggering prompt engineering reviews when quality signals degrade.

● Ensure human-in-the-loop engineering governance — every AI output reviewed and approved by accountable engineers, static-analysis quality gates enforced in CI/CD, and code maintainability standards upheld for client knowledge transfer.

● Drive the compounding improvement loop: feed early-phase learnings (client-specific patterns, domain idioms, codebase context) back into AI toolkits to accelerate each subsequent phase.

● Represent AI-native delivery economics credibly — significant velocity gains versus manual migration — with transparent, metrics-backed evidence to client and internal leadership.

Client & Stakeholder Management

● Serve as the primary delivery interface to client program decision authorities and executive sponsors — owning weekly status reporting, Monthly Business Reviews, phase-gate approvals, and multi-tier escalation models with defined SLAs.

● Manage client dependency governance: source code delivery, workplace provisioning, SME availability commitments,



and decision authority responsiveness — escalating proactively before dependencies become delays.

● Coordinate technology partner dimensions including cloud provider co-investment programs and architecture alignment with hyperscaler partners.

● Build stakeholder trust through proactive transparency — reports delivered before they are asked for, risks surfaced early with mitigation options, and commitments honored visibly.

Team Leadership & Delivery Organization

● Lead and develop delivery organizations of 50+ professionals — solution architects, full-stack engineers, AI/prompt engineers, QA automation specialists, business analysts, and project managers — organized into delivery pods across concurrent phases.

● Own capacity planning and pod mobilization across overlapping phases, including ramp plans, staffing approvals, and skill-mix decisions.

● Establish AI-era engineering culture: engineers as directors and reviewers of AI output, continuous prompt craft improvement, and quality ownership independent of generation method.

● Create career development pathways and succession depth for pod leads and senior engineers; retain critical program-context knowledge across the delivery lifecycle.

Practice & Capability Building

● Harvest program delivery into reusable modernization assets — AI migration methodologies, domain-specific rule patterns, accelerator libraries, and delivery playbooks — positioning the organization for the next wave of legacy modernization engagements.

● Contribute delivery evidence to case studies, reference architectures, and go-to-market assets (subject to client agreements) that differentiate the AI-native delivery practice.

● Mature delivery governance standards — metrics frameworks, business rule catalog templates, phase-gate checklists — into organizational IP applicable across programs.

📌 Associate Director — Delivery Leader (Hyderabad)
🏢 Genzeon Global
📍 Hyderabad