Senior AI/ML Backend Developer - Benchmarking and Scenario

Job Title: Senior AI/ML Backend Developer Benchmarking & Scenario

Employment Type:  Full Time

Position Location: Hyderabad           

Reports to: Delivery Head

Qualifications: BE/B.Tech/MCA Degree in Computer Science, Engineering, or similar relevant field 

Total Experience: 8 - 15 Years  

Working Model: Office

Shift Timing: 12pm – 9pm / 1pm to 10pm

About the Role

We are looking for Senior AI/ML Backend Developer to build and scale the systems that power our artificial intelligence features. You will write clean Python code, build strong API connections, and use Snowflake to manage large amounts of data.

Required Experience

• Strong Python backend and API development is essential, with depth in one service framework such as FastAPI, Flask or Django. The framework is not yet fixed for this build.
• Quantitative and statistical modeling in production matters most here, including weighted aggregation, peer-group comparison, index-based adjustment, and scenario modeling with several inputs moving at once.
• Advanced SQL and hands-on Snowflake, including performance and cost tuning, and querying schemas the client owns and controls.
• Relational data modeling, along with experience of an audit trail that holds up when someone challenges a decision months later. PostgreSQL is the proposed operational store, pending Compass confirmation against SQL Server, so either engine works.
• Experience building for traceability, where every derived figure can be explained back to its inputs without anyone reverse-engineering the code.
• AWS experience with containerized services and asynchronous processing, covering ECS, Step Functions, queues and event routing.
• Load and performance testing of APIs and query paths is part of the seat, whether with Locust, JMeter or profiling written in-house, along with a record of acting on the results.
• Git and continuous integration are assumed. This build runs on GitHub Enterprise with Actions and automated review gates.

Useful additions

• Pricing, revenue management, competitive intelligence or retail benchmarking as a domain background.
• Server-side document generation at volume, particularly PowerPoint and Excel produced from templates.
• Snowpark or in-warehouse Python. This is not a requirement and is named in no design document, but the Snowflake access pattern is still being settled.