Freelancer

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.