Senior Python/Django Developer

Location: 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?

Mandate Skills:

• 3 - 7 years of experience in cloud-native applications (Azure strongly preferred) using Python.
• Thorough understanding of Object-Oriented Programming (OOP) design and practices.
• Strong RDBMS experience (PostgreSQL, MS SQL etc.) designing tables & writing queries.
• Great command of design patterns (especially integration design patterns)

Azure DE

Primary Responsibilities -

• Create and maintain data storage solutions including Azure SQL Database, Azure Data Lake, and Azure Blob Storage.
• Design, implement, and maintain data pipelines for data ingestion, processing, and transformation in Azure Create data models for analytics purposes
• Utilizing Azure Data Factory or comparable technologies, create and maintain ETL (Extract, Transform, Load) operations
• Use Azure Data Factory and Databricks to assemble large, complex data sets
• Implementing data validation and cleansing procedures will ensure the quality, integrity, and dependability of the data.
• Ensure data security and compliance
• Collaborate with data engineers, and other stakeholders to understand requirements and translate them into scalable and reliable data platform architectures

Required skills:

• Blend of technical expertise, analytical problem-solving, and collaboration with cross-functional teams
• Azure DevOps
• Apache Spark, Python
• SQL proficiency
• Azure Databricks knowledge
• Big data technologies

The DEs should be well versed in coding, spark core and data ingestion using Azure. Moreover, they need to be decent in terms of communication skills. They should also have core Azure DE skills and coding skills (pyspark, python and SQL).

Out of the 7 open demands, 5 of The Azure Data Engineers should have minimum 5 years of relevant Data Engineering experience.

Job Description for Drools developer -

• Hands on experience in design and development in Drools rule engine (minimum 1.5 years) and Core Java
• Exceptional ability to communicate with both technical and non-technical people
• Expertise in object oriented programming
• Experience in Deploying and supporting core and advanced features
• Good exposure to SQL and/or Oracle/ MySQL,
• Candidate should be well verse with Agile processes
• Strong ability to work collaboratively across diverse groups of business and technical stakeholders is required.

We are looking for a Python Web Developer responsible for managing the interchange of data between the server and the users. Your primary focus will be the development of all server-side logic, ensuring high performance and responsiveness to requests from the front-end. You will also be responsible for integrating the front-end elements built by your co-workers into the application; therefore, a basic understanding of front-end technologies is necessary as well.

• Writing reusable, testable, and efficient code
• Design and implementation of low-latency, high-availability, and performant applications
• Integration of user-facing elements developed by front-end developers with server side logic
• Implementation of security and data protection
• Integration of data storage solutions