About the role
Ollive AI Inc is hiring a Fullstack Software Engineer to take ownership of integrations, data pipelines, and the architecture that ties together their live monitoring and synthetic testing stack. The engineering team is small and hands-on: a CTO, a senior fullstack engineer handling infrastructure, and one junior engineer on client deliverables and frontend. This is a contributor-level seat reporting directly to the CTO, with real scope to shape how a scaling platform is built from the inside.
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+ 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 is the core, but willingness to ship 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 toward one that can handle it.
- 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.