Senior GenAI Full-Stack Data Scientist (Remote)

Lingaro; Polska; Remote; PLN 466,000 - 700,000; Full time

Benefits offered by this job

Office as an option

Workation

Udemy access

Job summary

Lingaro is seeking a Senior Data Scientist focused on GenAI to lead architecture, development, and delivery of advanced LLM-based systems. You will design, implement, and optimize GenAI solutions, mentor teams, and guide technology choices for enterprise-scale AI projects.

You will build end-to-end GenAI applications including data ingestion, retrieval layers, orchestration, API/backend, and lightweight UI, while refining RAG pipelines and evaluation frameworks for robustness.

Qualifications

• 6+ years in Data Science/AI engineering.
• 4+ years production-ready Python AI code.
• 2+ years production-ready LLM code, preferably RAG.
• Strong communication and ability to mentor teams.

Responsibilities

• Lead GenAI use-case discovery and solution design.
• Build end-to-end GenAI applications (data ingestion, retrieval, orchestration, API/backend, UI).
• Design and implement RAG pipelines with vector databases and evaluation frameworks.
• Model selection, prompting strategies, and fine-tuning for multiple modalities.
• Collaborate with data engineers, product owners, and full‑stack developers on scalable architectures.
• Mentor other data scientists/engineers in GenAI patterns and best practices.

Skills

GenAI expertise

LLM deployment

Python production code

LangChain

Guardrails design

Cloud platforms

English communication

Team mentorship

Tools

LangChain

LlamaIndex

LangGraph

Job description

Lingaro is seeking a Senior Data Scientist focused on GenAI to lead architecture, development, and delivery of advanced LLM-based systems. You will design, implement, and optimize GenAI solutions, mentor teams, and guide technology choices for enterprise-scale AI projects.

You will build end-to-end GenAI applications including data ingestion, retrieval layers, orchestration, API/backend, and lightweight UI, while refining RAG pipelines and evaluation frameworks for robustness.