New opportunity

AI Workflow Engineer

ShopOS · Bangalore, India

About this role

Role Overview: As an AI Workflow Engineer at ShopOS, you will build and own the automation backbone for an AI-native commerce stack. You will design modular, node-based workflow systems that integrate across catalog management, creative tools, personalization engines, and fulfillment systems. This role requires a systems-thinking approach to solve complex logic puzzles and create reliable, scalable automations. Responsibilities: Build node-based, rule-driven workflows for eCommerce operations and AI agent interactions. Develop modular components for reusable logic-driven flows. Integrate with APIs (REST/GraphQL) and 3rd-party services like Shopify, Stripe, and Zapier. Diagnose, fix, and refactor workflows to improve reliability, speed, and cost-efficiency. Audit workflows to identify failure-prone flows and maintain comprehensive documentation. Set up admin interfaces and monitoring tools for performance tracking. Collaborate with product, design, and AI/ML teams to embed intelligence into decision points and ensure intuitive, testable workflows. Requirements: 1-4 years of experience (open to junior CS grads). Solid coding fundamentals with proficiency in JavaScript, TypeScript, and Python. Hands-on experience with visual workflow builders (Make, n8n, Zapier). Strong debugging discipline and ability to trace failures across connected systems. Familiarity with Figma, Weavy, and ComfyUI. Bonus Qualifications: Understanding of state machines, rule engines, event-driven architectures (Kafka, RabbitMQ, SQS), Postgres, Redis, and infrastructure-as-code (Docker, Terraform). Experience with low-code/no-code tools (Temporal, Langchain, Camunda, Retool) and AI/LLM systems is highly valued. Benefits: Competitive salary, health and wellness benefits, and direct mentorship from founders. This is an on-site role based in the Bangalore (HSR Layout) office.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

JavaScriptTypeScriptPythonMaken8nZapierFigmaWeavyComfyUIREST APIsGraphQLWorkflow EnginesState MachinesRule EnginesEvent-driven ArchitecturesKafkaRabbitMQSQSPostgresRedisDockerTerraformCI/CDTemporalLangchainCamundaRetool

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Modular Workflow Architecture
Design and implement node-based, rule-driven systems that are reusable and scalable. The priority is to move beyond one-off scripts to create a robust automation backbone that supports complex eCommerce operations and AI agent interactions.
System Reliability and Observability
Establish monitoring, audit tools, and admin interfaces to track workflow health. You must proactively identify failure-prone flows and maintain comprehensive documentation to ensure system stability as the platform scales.
Cross-System Integration
Seamlessly connect disparate services including Shopify, Stripe, and internal AI/ML engines via REST and GraphQL APIs. Success depends on your ability to maintain data integrity and logic flow across these external and internal dependencies.
Iterative Refactoring
Continuously diagnose, fix, and optimize existing workflows for speed, cost-efficiency, and reliability. This requires a disciplined approach to debugging and a commitment to improving legacy automations.

Likely challenges to think through

Debugging Distributed Failures
Because workflows span multiple APIs and third-party services, tracing the root cause of a failure is difficult. Prepare by demonstrating a systematic approach to logging, error handling, and state tracking across complex, multi-node architectures.
Balancing Code and No-Code
The role requires pragmatism in choosing between custom code and visual builders like Make or n8n. Prepare to discuss your decision-making framework for when to build a custom component versus leveraging existing low-code tools to maintain system maintainability.
Managing State in Event-Driven Systems
Handling complex logic in an event-driven environment can lead to race conditions or inconsistent states. Prepare by reviewing concepts like state machines, queueing systems (Kafka/SQS), and idempotent workflow design to ensure reliability.

Practice your answers

Technical practice

Designing Modular Workflow Components

What to coverExplain your approach to creating reusable, node-based components. Focus on how you ensure modularity, maintain state across nodes, and handle data passing between disparate systems like Shopify or Stripe.

Debugging Distributed Workflow Failures

What to coverDescribe your methodology for tracing a failure across a multi-step, event-driven workflow. Discuss how you use logging, monitoring, and audit trails to isolate whether a failure occurred in the API integration, the logic engine, or the external service.

State Machines vs. Rule Engines

What to coverCompare the use of state machines versus rule engines for managing complex eCommerce operations. Discuss scenarios where one is preferable over the other regarding scalability, predictability, and ease of maintenance.

Integrating AI/LLM Systems into Workflows

What to coverHow do you handle the non-deterministic nature of LLM outputs within a structured, rule-driven workflow? Suggestion: Focus on validation layers, fallback mechanisms, and how you structure prompts to ensure reliable downstream execution.

Optimizing Workflow Performance and Cost

What to coverWhen auditing a high-volume workflow, what metrics do you prioritize to identify bottlenecks or excessive costs? Discuss techniques for refactoring, such as batching requests or implementing caching with Redis.

Event-Driven Architecture Fundamentals

What to coverExplain the role of message queues like Kafka, RabbitMQ, or SQS in an automation backbone. Describe how you would design a system to handle asynchronous tasks to prevent blocking the main commerce stack.

API Integration Strategies

What to coverDiscuss the trade-offs between using REST and GraphQL when integrating third-party commerce services. Explain how you handle rate limiting, authentication, and error retries in a production environment.

Infrastructure as Code and Deployment

What to coverHow do you manage the deployment of workflow configurations and supporting infrastructure? Suggestion: Discuss the use of Docker and Terraform to ensure consistency between development, staging, and production environments.

Behavioural practice

Handling Systemic Failure Under Pressure

What to coverDescribe a time you had to diagnose and fix a critical failure in a live production system. Focus on your process for identifying the root cause, communicating with stakeholders, and implementing a permanent fix to prevent recurrence.

Balancing Speed and Technical Debt

What to coverGive an example of a situation where you had to choose between a quick, 'scrappy' fix and a more robust, long-term architectural solution. Explain your decision-making process and how you managed the resulting technical debt.

Cross-Functional Collaboration

What to coverDescribe a project where you worked closely with non-technical teams like design or product to build an internal tool. How did you translate their business requirements into technical specifications while ensuring the final output remained intuitive?

Taking Ownership of Legacy Systems

What to coverTell me about a time you inherited a complex, poorly documented system. What steps did you take to understand the logic, improve the documentation, and eventually modernize or refactor it?

Sharpen your knowledge

Topics to prepare

State Machines and Rule Engines
The role emphasizes building node-based, rule-driven workflows. Review the conceptual design of finite state machines (FSM) and how they manage transitions in complex systems. Exercise: Model a simple e-commerce order fulfillment process as a state machine, defining states (e.g., Pending, Paid, Shipped) and the triggers that cause transitions.
Event-Driven Architecture
Understanding how systems communicate asynchronously is critical for scaling automation. Review concepts like message queues, pub/sub patterns, and event consistency. Exercise: Design a conceptual architecture where a Shopify webhook event triggers a series of downstream actions in an AI agent, identifying where a queue (like SQS or RabbitMQ) would prevent data loss.

Skills to revise

Visual Workflow Automation
The company uses Make and values experience with n8n or Zapier. Exercise: Build a multi-step workflow in n8n or Make that takes a JSON input, transforms the data using a JavaScript node, and sends a conditional request to a REST API. This demonstrates your ability to bridge low-code tools with custom logic.
API Integration and Debugging
You will be responsible for integrating and troubleshooting third-party services. Exercise: Create a script in TypeScript or Python that interacts with a public API (e.g., GitHub or OpenWeather), implements error handling for rate limits, and logs the full request-response cycle to a local file to practice tracing failures.
Database and Caching Fundamentals
Familiarity with Postgres and Redis is a bonus for managing workflow state and performance. Exercise: Write a SQL query to join two tables representing users and orders, then explain how you would use Redis to cache the result of a frequently accessed, expensive query to improve workflow speed.

Your preparation checklist

Prepare a modular workflow case study
Create a portfolio piece demonstrating a node-based workflow you built using tools like n8n or Make. Focus on how you handled error states, data transformation between APIs, and modular component design to ensure reusability.
Demonstrate debugging discipline
Prepare a walkthrough of a complex system failure you diagnosed. Explain your methodology for tracing issues across distributed systems or API integrations, highlighting how you identified the root cause and implemented a permanent fix.
Review state machine and event-driven concepts
Brush up on state machine logic and event-driven architecture principles. Be ready to discuss how you would design a self-healing workflow that handles retries, state persistence, and queueing using tools like Redis or SQS.
Develop technical questions for the team
Prepare questions regarding their current workflow stack, such as how they manage version control for visual workflows, their strategy for monitoring workflow latency, or how they balance hard-coded logic versus low-code components.
Articulate your approach to AI integration
Reflect on how you would integrate LLMs into a workflow. Prepare to discuss how you manage prompt chaining, handle non-deterministic AI outputs within a rigid business process, and ensure testability in AI-driven decision points.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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