Deployment Lead

Project Role : Deployment Lead Project Role Description : Plan and lead the execution of a comprehensive deployment plan, including work planning, scheduling, budgeting, metrics, training, pilots, and resources. Collaborate with all project teams to manage interdependencies, ensure alignment between all deployment-related activities, and monitor & control progress through the deployment plan. Must have skills : SAP BTP Integration Suite Good to have skills : NA Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education

AI Powered Tech talent Summary Build AI native, enterprise integration products on SAP BTP Integration Suite by combining strong integration engineering fundamentals with agentic AI patterns (LLMs + tools + retrieval + evaluation). The role focuses on creating and operating production-grade integrations across cloud, on premise, and hybrid landscapes—while augmenting the integration lifecycle with intelligent capabilities such as automated iFlow generation, mapping recommendations, anomaly detection, predictive operations, and conversational troubleshooting. This role owns AI behavior in the integration layer (grounding, safety, evaluation), not just AI-assisted development.

Core Responsibilities 1) Integration Engineering (Cloud, Hybrid, On Prem) Design, develop, and run end-to-end integration scenarios using cloud integration capabilities, enabling reliable connectivity across SAP and non SAP systems. Build, structure, and manage large integration flows with clear modularity, reusability, and maintainability for enterprise scale. 2) Integration Flow Development (iFlows) & Message Transformation Create and maintain integration flows using routing, splitting, gathering, and other transformation patterns. Implement message mappings and data transformations for common enterprise formats (e.g., JSON, XML, CSV), ensuring semantic correctness and downstream compatibility. Apply canonical modeling and contract-first practices to reduce brittle point-to-point dependencies. 3) Scripting, Persistence & Advanced Flow Control Use scripting (e.g., Groovy) to implement complex transformation logic, enrichment, validation, and conditional execution paths. Implement message persistence patterns (e.g., data stores) to support idempotency, retries, correlation, and resilience. 4) API & Connector Enablement Design and expose secure APIs through API management capabilities (policies, authentication/authorization, throttling, versioning). Integrate with third party applications using connector-based approaches and standardized integration patterns. Enable event-driven integration approaches using messaging/event capabilities for near real-time propagation and decoupled architectures. 5) AI Native Integration Intelligence (Agentic Integration Layer) Build integration agents that can generate initial iFlows from system/endpoint selections and naming conventions, then validate them against enterprise standards. Implement retrieval-grounded assistance that pulls from interface catalogs, mapping specs, standards, runbooks, and known incident patterns to produce verifiable recommendations (no free-form guessing). Apply intelligent recommendations for mapping and configuration, accelerating build and reducing rework. Develop AI-driven anomaly detection and prediction for integration/API behavior (error spikes, latency patterns), with human-in-the-loop controls for remediation actions. Optimize and analyze scripts used in integration flows for performance and maintainability through AI-assisted code analysis techniques. 6) Testing, Quality Engineering & Evaluation Loops Build automated testing strategies for integrations: schema/contract validation, payload regression suites, replay testing, and API testing tool-driven verification. Implement AI evaluation harnesses (offline golden test sets + online monitoring) for generated mappings, generated flows, and operational recommendations. Establish release gates for both integration changes and AI prompt/tool updates to prevent silent degradation. 7) Monitoring, Troubleshooting & Observability Set up monitoring dashboards and alerts for flows, APIs, and event-driven pipelines establish traceability with correlation IDs and end-to-end tracking. Diagnose and resolve integration issues using structured logs, message traces, and failure-mode analysis. Create AI-native operational experiences such as conversational root-cause analysis, runbook-guided remediation, and impact analysis—bounded by role-based access and approval workflows. 8) Integration Strategy & Maturity Enablement Apply integration advisory practices to design hybrid integration platforms and define integration best practices across teams. Use integration assessment approaches to improve integration maturity, standardize patterns, and automate repeatable architectural governance where feasible.