We are seeking a Senior Software Engineer to modernize a large SAP Commerce Cloud platform through AI-assisted engineering, context engineering, and durable workflow orchestration.
A central responsibility of this role is designing business processes that remain reliable across service failures, deployments, retries, and long execution periods. The engineer should understand Temporal-style programming models and be able to apply the same principles using Temporal, Cadence, Azure Durable Functions, Conductor, or a comparable platform.
This is a hands-on engineering role-not an AI strategy or prompt-engineering position.
Responsibilities
Develop and support SAP Commerce Cloud capabilities using Java and Spring.
Build Integrations with OCC and REST APIs.
Design durable workflows for long-running commerce processes such as commerce cart work flows, order orchestration, inventory updates, and recovery operations.
Separate deterministic orchestration logic from side-effecting activities.
Prevent unbounded workflow histories through appropriate continuation and lifecycle strategies.
Build automated workflow, activity, replay, integration, and failure-path tests.
Integrate durable workflows with SAP Commerce, Kafka, APIs, databases, and external services.
Apply AI and context engineering to code discovery, implementation, testing, documentation, incident response, and release analysis.
Build reusable AI instructions, agent skills, MCP integrations, repository context, and verification guardrails.
Diagnose SAP Commerce performance issues involving FlexibleSearch, persistence, caching, TaskEngine, Kafka consumers, and database contention.
Required Qualifications
Senior-level experience delivering distributed production systems.
Strong SAP Commerce Cloud/Hybris or similar platform development experience.
Advanced Java and Spring development skills.
Experience with React.js and modern JavaScript or TypeScript.
Hands-on experience with at least one durable orchestration platform, such as Temporal, Cadence, Azure Durable Functions, AWS Step Functions, or Conductor.
Strong understanding of:
Workflow determinism and replay
Workflow versus activity responsibilities
At-least-once execution semantics
Idempotency and duplicate prevention
Retry classification and non-retryable failures
Compensation and saga patterns
Durable timers and asynchronous events
Workflow versioning and backward compatibility
Failure recovery and operational visibility
Experience designing event-driven integrations using Kafka or comparable messaging technology.
Demonstrated use of AI coding assistants or agentic engineering tools on substantive software work.
Practical understanding of context engineering: equipping AI systems with appropriate domain knowledge, tools, instructions, examples, and validation.
Strong automated testing, debugging, performance-analysis, and production-support skills.