Experience: 1 to 3 years

Life at CultureMonkey:https://www.culturemonkey.io/life-at-culturemonkey/

About the Role

CultureMonkey is an employee engagement platform serving HR and leadership teams globally. Our product is a multi-tenant Ruby on Rails application with a schema-per-tenant PostgreSQL setup, Elasticsearch-backed analytics, a ClickHouse warehouse, and a large background job layer.

We are hiring an engineer who ships features independently and uses AI tooling as a genuine force multiplier. If your idea of AI-assisted development is pasting generated code into a PR, this is not the role for you. If it is decomposing a problem, driving an agent through it, verifying the output against tests and production data, and shipping in a third of the time, read on.

What You Will Do

- Own features end to end: scoping, schema design, implementation, testing, rollout behind feature flags, and monitoring after release
- Write and maintain code across the stack: Rails models and services, Grape APIs, background jobs, Elasticsearch queries, and reporting pipelines
- Design queries and data access patterns that hold up at scale
- Use AI tools to increase throughput across exploration, refactoring, test coverage, migrations, and documentation, and help set the standard for how the team uses them
- Improve the codebase you touch: eliminate duplication, tighten boundaries, and document non-obvious flows

What We Are Looking For

- Engineers with 2+ years of experience building and maintaining production web applications in any language or framework. Ruby on Rails experience is a plus, not a requirement. Solid Python, Java, Go, Node.js, or PHP engineers who want to move to Rails are welcome
- Strong SQL skills: joins, aggregates, indexing, query plans, and understanding why a query became slow




- Comfortable with REST API design and authentication and authorization concepts
- Practical experience with background jobs, queues, or asynchronous processing
- You write tests as a matter of habit, not as a checkbox
- You debug systematically: reproduce, isolate, verify, then fix

AI Tooling (Non-Negotiable for This Role)

- Daily, demonstrated use of AI coding assistants in real production work
- You can articulate where AI helps and where it actively hurts, and you have opinions formed from experience
- You verify AI output: run it, test it, review it, and reject it when it is wrong
- You are interested in improving how the whole team works with these tools, not just your own output

Bonus Points

- Multi-tenant SaaS experience (schema-, row-, or database-level isolation)
- Experience with Elasticsearch, ClickHouse, Kafka, or analytics/warehouse systems
- Experience with SSO/SAML, HRIS integrations, or third-party API integrations at scale
- Experience building anything on top of LLM APIs: RAG, agents, evals, or internal developer tooling

How We Evaluate

- Resume and portfolio screen
- Technical discussion: system design and data modelling on a realistic problem from our domain
- Practical round: a scoped task in our stack. AI tools are allowed and encouraged. We will review the code with you and probe your decisions
- Depth round on your past production work: an incident you owned, a scaling problem you solved, and a decision you would now make differently
- Founder/leadership discussion

What You Get

- Direct product ownership with a short path from idea to production
- An engineering culture that works with AI tooling seriously, not as a novelty
- A codebase with real engineering problems: multi-tenancy, scale, analytics, and integrations

📌 Software Engineer (Chennai)
🏢 CultureMonkey
📍 Chennai