Software Engineer — AI Agent Platform / Polyant

About Exelab and Polyant

Exelab builds custom, AI-powered solutions around real business needs. We work in small teams and use AI directly in how we design and build software.

You will join the team building Polyant, our open-source, self-hosted runtime for AI agents. It brings together memory, retrieval over business documents, tool and plugin execution, messaging channels, permissions, tracing and cost control, behind an OpenAI-compatible API and an administration interface.

Explore the product before applying: https://polyant.ai/

Documentation: https://docs.polyant.ai/

Code: https://github.com/polyant-ai/polyant

The role

Your primary focus will be Polyant: building features, improving reliability and helping shape the product over time. You will work across the platform, from the administration interface and runtime to the database and release pipeline, taking ownership of defined product areas in collaboration with the team.

This role suits an engineer with solid experience shipping and maintaining production software, who can turn an ambiguous need into a scoped proposal, explain architectural trade-offs and follow a change through to release. Ownership includes maintenance, documentation and learning from failures.

Research and experimentation are part of the work. You will test ideas around agent behaviour, retrieval, memory and evaluation, then turn promising results into maintainable product capabilities. The focus is software and agent engineering; model training, fine-tuning and MLOps are outside this role's scope.

What you will do

• Design and deliver features end to end, from the data model and APIs to the user interface, tests and documentation.

• Own the technical decisions within your areas and make explicit trade-offs between user value, implementation effort, reliability and maintenance cost.

• Develop agent capabilities such as tool execution, context management, retrieval and memory, with clear handling of failure modes.

• Protect a multi-tenant platform through sound authorization, data isolation, input validation and careful review.

• Build regression tests and agent evaluations; use traces, latency and cost measurements to assess changes.

• Keep releases dependable through CI/CD, safe database migrations, deployment practices and investigation of production issues.

• Use AI coding agents deliberately: provide context and constraints, split work into reviewable changes, and verify generated code against the intended behaviour.

• Propose product improvements and share experiments, decisions and findings with the team.