Your responsibilities will include:

Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
Establishing MLOps practices, including orchestration, observability, and experiment tracking.
Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
Defining hiring criteria, growing the ML team, and shaping the ML team culture.

We expect you to have:

A proven track record as an ML/AI Lead.
At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
Hands-on experience with:

Prompt engineering and LLM pipeline design, including evaluation.
Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
Vector databases and retrieval-augmented generation (RAG) patterns.
Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.

Strong Python skills – Kotlin knowledge would be a plus.
Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
Proficiency in English, both written and verbal.

Experience with ontologies, knowledge graphs, or graph-based reasoning.
Experience in early-stage startups – you enjoy the zero-to-one phase.
The ability to think strategically about product-led AI, beyond just training models in isolation.
A background in code analysis, developer tools, or software engineering research.
Experience with multi-agent systems or complex agentic workflows.
Actively contributed to relevant open-source projects or publications.