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Requirements
- 12+ years of overall SAP consulting/architecture experience, with at least 4–5 years on SAP BTP and its predecessor (HCP/SCP).
- Minimum 3 full-cycle SAP BTP implementation projects as Solution/Integration Architect or Technical Lead in large enterprise environments.
- Hands-on delivery experience with SAP AI Core or at least 2 production AI/ML implementations in the SAP ecosystem (not just POC/demo).
- Deep familiarity with SAP S/4HANA cloud integration patterns, side-by-side extensibility, and ABAP-to-BTP migration strategies.
- Experience leading architecture design sessions, reviewing technical deliverables, and owning solution blueprints for enterprise clients.
- Exposure to multi-cloud environments and ability to design hybrid landscapes integrating SAP BTP with hyperscalers.
- Prior client-facing consulting or practice-building experience in a Big 4, SAP partner, or independent software vendor context preferred.
Roles & Responsibilities
- Contribute to IP creation — accelerators, reusable assets, reference architectures, and practice-building initiatives.
- Design BTP solution architectures covering SAP Integration Suite, Extension Suite, Data and Analytics cloud, and SAP Build portfolio.
- Architect and deploy SAP AI Core pipelines — model serving, ML Ops workflows, and custom inference endpoints in production.
- Lead development of cloud integration flows using SAP Integration Suite (CPI), API Management, and Event Mesh for hybrid and multi-cloud landscapes.
- Drive SAP BTP subaccount strategy, environment provisioning, identity and access management (IAS/IPS), and security posture.
- Define and govern integration patterns — synchronous/asynchronous, event-driven, point-to-point, and hub-and-spoke models.
- Enable AI use cases using SAP AI Launchpad, Foundation Models, and third-party LLM integrations via BTP Generative AI Hub.
- Build and mentor a team of consultants and developers; run architecture workshops, enablement sessions, and CoE activities.
- Evaluate and recommend SAP BTP service mix against custom cloud-native alternatives to optimize TCO and roadmap alignment.
- Stay current with SAP roadmap, RISE with SAP, GROW with SAP, and relevant cloud platform evolution (AWS, Azure, GCP).
Certifications
- Preferred / expected - C_BTP_2408 — SAP Certified Associate, BTP ; P_BTP_2408 — SAP Certified Professional, BTP ; C_AIG_2412 — SAP AI & GenAI ; C_ARCIG_2404 — SAP Integration Solution Architect ; C_HANADEV — SAP HANA Cloud Developer
- Good to have - AWS Solutions Architect, Azure / GCP Professional ; SAP Analytics Cloud
Java Backend Developer (6–10 Years)
Mandatory Skills:
- Core Java 8+ (Collections, Streams, Multithreading)
- Spring Boot
- Microservices Architecture
- RESTful API Development
- SQL (Oracle/MySQL/PostgreSQL)
- JPA/Hibernate
- Design Patterns & OOP
- Distributed Systems
- Kafka/RabbitMQ (Preferred)
- AWS/Azure (Preferred)
- CI/CD, Git, Jenkins, Docker/Kubernetes (Preferred)
Responsibilities:
- Design and develop scalable backend microservices.
- Build secure and high-performance REST APIs.
- Work on distributed systems and production-grade applications.
- Optimize application performance and troubleshoot production issues.
- Participate in code reviews and Agile development.
Preferred Background:
- Product engineering or enterprise backend development.
- Strong coding skills in Java with hands-on experience in live problem solving.
Experience: 6–10 Years
Location: Bangalore / Mumbai / Chennai (Hybrid)
Primary Skills
- Observability: ELK (Elasticsearch/Kibana), Prometheus, Grafana, PromQL
- Automation: Java/Vert.x or Python (FastAPI), Shell/Bash, REST/SOAP APIs
- Cloud & Platform: Docker, Kubernetes, Kafka, Redis
- Reliability Engineering: Distributed Systems, Microservices, Event-Driven Architecture, DR & Incident Management
- Stakeholder Management & Cross-functional Collaboration
Secondary Skills
- Agentic AI: LangChain, LangGraph, RAG, MCP
- LLM Integration & AI Frameworks
- Python (FastAPI)
Key Responsibilities
- Build and deploy LLM-powered Agentic AI solutions with tool calling and autonomous workflows.
- Integrate AI capabilities into existing applications using modern AI frameworks.
- Own platform reliability through SLAs, SLOs, error budgets, MTTD/MTTR, and operational governance.
- Enhance observability using ELK, Prometheus, Grafana, and advanced alerting.
- Lead incident response, RCA, disaster recovery, and resiliency initiatives.
- Drive production readiness, automation, platform stability, and infrastructure optimization.
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