PythonLocation: Bangalore
Experience: 2–5 years
Type: Full-time | On-site
Open Roles: 2
Start: Immediate
Why this role exists
Most systems work at a low scale.
Very few survive real production load, complex workflows, and enterprise edge cases.
We are building a platform that must:
- Scale from 500K → 20M+ interactions/month
- Handle complex insurance workflows reliably
- Become easier to deploy as it grows, not harder
This role exists to build the backend foundation that makes this possible.
What you’ll do
You will not just write services.
You will design and own core platform systems.
1. Scale the platform without breaking architecture
- Scale from 50K → 2M+ interactions/month
- Ensure:
- High availability
- Low latency
- Fault tolerance
- Avoid large rewrites — build systems that evolve cleanly
2. Build the workflow automation (WA) engine
- Design a flexible system with:
- States
- Stages
- Cohorts
- Dynamic workflows
- Ensure workflows:
- Handle edge cases reliably
- Can be configured easily
- Move from:
- Hardcoded flows → configurable execution engine
3. Build the insurance-specific data layer
- Design data models for:
- Policy states
- Claim workflows
- Consent tracking
- Ensure the system works across:
- Multiple insurers
- Multiple use cases
- Build a platform-first data layer, not use-case-specific hacks
4. Make deployment and setup simple
- Ensure workflows and data models are:
- Easy to configure
- Easy to launch
- Reduce friction for:
- Product teams
- Deployment teams
5. Create a compounding data advantage
- Build a data layer that:
- Improves with every deployment
- Captures structured signals
- Ensure data becomes a long-term edge, not just storage
6. Own production reliability
- Participate in on-call rotation across 3 engineers
- Ensure:
- Incidents are handled quickly
- Root causes are fixed permanently
- Build systems where reliability is shared, not individual
What success looks like
- Platform scales to 2M+ interactions/month smoothly
- Workflow engine supports complex, dynamic use cases
- Data layer enables fast deployment across accounts
- Edge cases are handled without constant firefighting
- System becomes easier to use as it grows
- Production issues are rare and predictable
Who you are
- You have 2-5 years of backend engineering experience
- You have built:
- Scalable systems
- Distributed services
- You think in:
- Systems
- Data models
- Trade-offs
- You are comfortable owning:
- Architecture
- Production systems
What will make you stand out
- Experience building:
- Workflow engines
- State machines
- Data-heavy platforms
- Strong understanding of:
- System design
- Distributed systems
- Failure handling
- Experience working in:
- High-scale production environments
Why join
- You will build the core backend of an AI platform
- Your work directly impacts:
- Scale
- Reliability
- Product capability
- You will design systems that move from:
- Use-case specific → platform-level infrastructure
What this role is not
- Not just API development
- Not limited to feature-level work
- Not disconnected from production realities
What this role is
- A system architect
- A builder of scalable platforms
- A driver of long-term technical advantage
One question to self-evaluate
Can you design backend systems that scale, handle edge cases, and become easier to use as they grow?
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon