Take ownership of deploying, integrating, and maintaining company products in client environments.
Use technology to solve business-critical problems.
Focus on improving risk detection, fewer false positives, faster decisions, better explanations, and lower cost per case.
responsibilities :
Engaging directly with users - observing their workflows, understanding current processes, constraints, and desired improvements.
Defining measurable hypotheses and KPIs such as precision/recall, false-positive reduction, turnaround time, coverage, and cost efficiency.
Designing decision pipelines combining LLMs, retrieval systems/RAG, classic ML components, and lightweight rules - all with auditability and explainability in mind.
Prototyping fast - from notebooks to small APIs/services - and validating results on real datasets.
Preparing evaluation datasets and scoring rubrics including offline tests, side-by-side comparisons, sanity checks, and guardrails.
Presenting outcomes to business leaders and proposing rollout strategies (pilot → production-lite → full deployment).
Driving internal innovation, sharing findings, mentoring peers, and promoting effective AI practices across the company.
requirements-expected :
Proven delivery track record 2–3 concrete cases where your AI/ML work significantly improved key metrics in any domain.
Strong communication and problem-solving skills; ability to explain risks to non-technical stakeholders.
Practical experience with LLMs & ML: RAG, prompting, tool-calling, classification/ranking/deduplication, fundamentals of evaluation & experimentation
Python + SQL sufficient to build and maintain working prototypes.
Fluency in English and Polish for user interviews and concise documentation.
offered :
Full autonomy and end-to-end ownership.
Impact-focused engineering — outcomes over stack.
Real users, real data, real decisions.
Remote forever — no offices, ever.
Direct work with the CTO, high visibility, real influence on key decisions.
