Full Stack Engineer

Ollive AI Inc. is hiring a Full-stack 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 full-stack engineer handling infrastructure, and one junior engineer on client deliverables and the 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 to 5 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 willingness to ship a React frontend is 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.