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Ollive

Posted 28 Aug 2026

Full - Stack Engineer

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Employment
Full time
Team
Software Engineering
Posted
28 Aug 2026

About the role

Responsibilities:

  • Own integrations with existing tools across Ollive's stack, getting them up and running and plugging them into live monitoring and synthetic testing pipelines.
  • Establish and maintain Kafka-based data pipelines and data stores across Postgres and ClickHouse.
  • Design and evolve the backend architecture in Python and Django toward a system that can scale with customer growth.
  • Build and ship full-stack product surfaces in React as the scope demands, including frontend components tied to client deliverables.
  • Run inference at scale across Ollive's production lines and ensure throughput holds as the platform expands to more healthcare customers.
  • Contribute to the continuous-observability layer, connecting live monitoring of customer AI agents to the broader risk platform.


Requirements:

  • 1 - 4 years of hands-on software engineering experience, with the majority of that time writing production code rather than relying on AI coding assistants.
  • Proficiency in Python as the primary backend language, with practical experience in backend frameworks (Django preferred).
  • Demonstrated experience building and owning data pipelines and integrations with third-party tools in a production environment.
  • Comfort working across the stack: backend data and API work are the core, but be willing to ship a React frontend when the scope requires it.
  • Experience working with relational and analytical data stores such as Postgres and ClickHouse, or equivalent.
  • Ability to think in scalable architectures: the candidate should have seen systems that grew beyond their initial design and know how to move towards one that can handle them.
  • Willingness to work onsite in Bangalore six days a week at startup intensity.


Nice-to-Have:

  • Familiarity with Kafka or other event-streaming infrastructure for real-time data pipelines.
  • Exposure to AWS for cloud deployments and services.
  • Experience with inference at scale, including running model outputs through production pipelines.
  • Background at an early-stage startup where they built things without a defined playbook.

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