Design, build, and ship Generative AI capabilities that power tools for technical Q&A and data understanding.
Develop agentic workflows (planning, tool use, retrieval, execution, verification) with measurable quality, reliability, and cost controls.
Build evaluation systems for GenAI: offline test sets, automated scoring, human review loops, regression suites, and production monitoring.
Deliver production-ready services and pipelines (APIs, background jobs, data processing, configuration, release workflows) with strong engineering discipline.
Partner with product, engineering, and domain experts to translate customer problems into milestones, acceptance criteria, and clear success metrics.
Improve system quality through testing, observability, performance optimization (latency and scalability), and guardrails.
Contribute to an engineering-first culture: thoughtful design docs, code reviews, and pragmatic technical decisions.

6–10+ years of relevant experience in applied data science and Generative AI/LLM product development, including significant responsibility for production delivery.
Strong programming skills in Python, with modern engineering practices (testing, packaging, CI/CD, APIs, code reviews).
Proven experience delivering GenAI/LLM applications into production (not just prototypes), including reliability and operational considerations.
Hands-on experience building agents and using agent frameworks (e.g., LangChain, Agno, or similar tool-using agent stacks).
Delivery-oriented and comfortable owning ambiguous problems end-to-end: scoping, implementation, rollout, and iteration.
Independent, autonomous, self-driven, and proactive in a remote team; able to take a problem area and drive it to outcomes with minimal oversight.
Clear communicator: able to explain trade-offs, write crisp documentation, and align stakeholders.
Deeply curious and committed to staying up to date with the latest developments in Generative AI and agent frameworks, recognizing the field evolves rapidly.

Fully remote role with a remote-first team culture (well-suited to Europe and South Africa time zones).
High-ownership work on a customer-facing AI product that solves real operational and technical problems.
Environment that values shipping, measurable impact, and maintainable systems.
Competitive, region-aligned compensation and meaningful opportunities to grow scope based on impact.

Data Scientist

Key skills