Custom Software Engineer

What matters most

• Architecting Agentic Commerce Ecosystems

  The role requires designing and implementing multi-agent patterns and RAG pipelines that integrate directly with enterprise commerce platforms like Salesforce, Adobe, or SAP. You must demonstrate the ability to bridge the gap between traditional commerce data structures (OMS, PIM, CRM) and modern LLM-powered applications.

• Enforcing Technical and AI Governance

  Beyond design, you are responsible for ensuring production-grade reliability. This involves implementing AI observability, audit logging, and human-in-the-loop protocols, while actively preventing implementation drift through rigorous code reviews and adherence to architectural standards.

• Standardizing Delivery Patterns

  You are expected to transform implementation experiences into repeatable reference patterns and accelerators. This requires a focus on composable commerce (MACH) and API-first design to ensure that AI integrations are scalable and maintainable across multiple client projects.

Likely challenges to think through

• Balancing Innovation with Enterprise Stability

  Integrating experimental agentic AI frameworks with mission-critical enterprise systems creates a high risk of instability. Prepare to discuss how you manage the trade-off between rapid AI prototyping and the strict uptime and data integrity requirements of commerce platforms.

• Managing Implementation Drift

  As a technical authority, you will face pressure to deprioritize governance and observability features to meet delivery deadlines. Prepare examples of how you have successfully enforced architectural integrity and security guardrails under tight project constraints.

• Navigating Complex Integration Protocols

  The role requires familiarity with emerging agentic protocols (MCP, A2A, ACP). The challenge lies in the lack of industry-wide standardization for these protocols; be prepared to explain how you evaluate and adopt new integration standards while maintaining compatibility with legacy enterprise backends.

Topics to prepare

• Agentic Commerce Architecture

  Focus on the integration of autonomous AI agents within enterprise commerce ecosystems (OMS, PIM, CRM). Exercise: Draft a high-level architectural diagram showing how an AI agent interacts with a PIM system to retrieve product data and execute a transaction via an OMS, incorporating human-in-the-loop protocols.

• AI Governance and Observability

  Understand the requirements for production-grade AI, specifically audit logging, tracing, and guardrails. Exercise: Define a monitoring strategy for a RAG pipeline that includes specific metrics for retrieval accuracy and latency, and describe how you would implement a human-in-the-loop escalation trigger.

• Composable Commerce (MACH Principles)

  Study Microservices, API-first, Cloud-native, and Headless (MACH) patterns. Exercise: Explain how a headless commerce architecture facilitates the integration of LLM-powered applications compared to a monolithic platform, focusing on API-first design.

Skills to revise

• RAG Pipeline Implementation

  Hands-on development of retrieval-augmented generation systems. Exercise: Build a small prototype using LangChain or LlamaIndex that retrieves product information from a mock JSON database to answer customer queries, ensuring the context is relevant and accurate.

• Agentic Frameworks (LangGraph/CrewAI)

  Practical application of frameworks for multi-agent orchestration. Exercise: Create a simple multi-agent workflow where one agent handles search queries and a second agent validates the output against business rules before presenting it to the user.

• Hyperscaler AI Platforms

  Experience with cloud-native AI services like AWS Bedrock Agents, Google Vertex AI Agent Builder, or Azure AI Foundry. Exercise: Research the specific deployment steps for an agent on one of these platforms and document how you would manage its API authentication and data access permissions.

• Map Enterprise Commerce to Agentic Workflows

  Prepare a technical walkthrough demonstrating how you have integrated an enterprise commerce platform (e.g., Salesforce, SAP, or Adobe) with an LLM-powered application. Focus on how you handled data retrieval from PIM or OMS systems to feed a RAG pipeline.

• Demonstrate Multi-Agent Orchestration

  Create a brief architectural diagram or code sample using a framework like LangGraph or CrewAI. Explain how you managed agent state, tool usage, and human-in-the-loop protocols to ensure production-grade reliability.

• Define AI Governance and Observability Strategy

  Prepare to discuss your approach to implementing guardrails, audit logging, and monitoring for AI agents. Be ready to explain how you ensure architectural coherence and prevent implementation drift in high-pressure delivery environments.

• Prepare Hyperscaler AI Platform Use Cases

  Review your experience with AWS Bedrock Agents, Google Vertex AI Agent Builder, or Azure AI Foundry. Be prepared to discuss the trade-offs between using these managed services versus building custom agentic frameworks for commerce.

• Formulate Strategic Questions for the Interviewer

  Prepare questions regarding how the Commerce and AI practice balances rapid prototyping with long-term architectural standards, and how the team currently handles the integration of emerging protocols like MCP or A2A into existing MACH-based ecosystems.