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What you will do
- Build production-grade LLM and agentic workflows for business decision-making.
- Turn customer, market, and operational data into reasoning contexts that models can use reliably.
- Design evaluation, tracing, replay, and quality-control systems for nondeterministic AI outputs.
- Work on counterfactual analysis, forecasting, customer behaviour modelling, and decision support.
- Improve reliability through structured outputs, retrieval, constraints, calibration, tests, and model comparisons.
- Build internal tools that make AI workflows repeatable across customers and use cases.
- Help define how we measure whether a prediction was useful, accurate, and trustworthy.
- Work directly with the founders on product direction, customer problems, and technical architecture.
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What we are looking for
You may be a strong fit if you have:
- 5+ years of engineering experience.
- Strong Python skills and comfort building production systems.
- A solid ML, data science, or applied AI background.
- Recent hands-on experience with LLMs, agents, RAG, tool use, or AI workflow systems.
- Experience designing evaluations, tests, metrics, or quality gates for AI/ML systems.
- Good judgment around hallucination, uncertainty, model failure modes, and measurement.
- Comfort working with messy data, SQL, APIs, and data pipelines.
- The ability to debug both code and reasoning.
- Strong product instincts: you care whether the output actually helps a user make a decision.
- High agency in ambiguous environments.
- Clear communication and intellectual honesty.
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Especially relevant experience
We would be particularly interested if you have worked on any of the following:
- LLM applications or agentic systems in production.
- Evaluation systems for LLMs or ML models.
- Applied ML systems for forecasting, experimentation, analytics, recommendations, or decision intelligence.
- RAG, embeddings, vector search, structured extraction, or model orchestration.
- Statistical analysis, A/B testing, causal inference, or calibration.
- Data products where traceability, auditability, or reliability mattered.
- Early-stage startups where you had to own both technical direction and implementation.
Disclaimer: Calyptus uses an automated assessment tool that scores applicants.
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