Fullstack Senior Data Scientist (GenAI)

TL;DR

Fullstack Senior Data Scientist (GenAI): Lead projects focused on building and implementing advanced systems based on LLMs, chatbots, AI agents, and RAG mechanisms with an accent on end-to-end GenAI applications and architectural decisions. Focus on designing RAG pipelines, model selection and fine-tuning, safety controls, and mentoring teams.

Location: Poland (“Office as an option” – remote or in the office)

Company

Global company since 2008 with 1500+ talents across 7 sites, Great Place to Work certified.

What you will do

• Lead discovery and solution design for GenAI use cases, translating business problems into architectures including LLMs, RAGs, fine-tuning, agents, and guardrails.
• Build end-to-end GenAI applications covering data ingestion, retrieval, orchestration (LangChain/LlamaIndex/LangGraph), backend APIs, and simple UIs.
• Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks.
• Perform model selection, prompting, fine-tuning (LoRA/QLoRA/SFT) for text/code/multimodal models with evaluation and A/B testing.
• Implement safety, compliance, and governance controls like filters, PII handling, audit logs, and human-in-the-loop.
• Collaborate with data engineers, product owners, and developers on scalable architectures and integrations; mentor teams and contribute to reusable components.

Requirements

• 6+ years in Data Science/AI engineering, 4+ years production Python AI code, 2+ years production LLM code (preferably RAG).
• Strong Generative AI knowledge: LLMs, chatbots, agents, RAG, evaluators, validators, guardrails.
• Hands-on with GenAI frameworks (LangChain, LlamaIndex, LangGraph) and MCP servers/clients for LLM agents.
• Strong Python for production ML services; ML/AI concepts, frameworks, model lifecycle.
• Familiarity with cloud (Azure preferred), CI/CD, containerized deployments.
• Excellent English communication skills to convey complex concepts.

Nice to have

• ML algorithms and data pipelines in Python.
• CI/CD and DevOps tools (GitHub Actions, GitLab, Azure DevOps).
• Productizing ML with Spark/Databricks, Docker/Kubernetes.
• Agentic AI frameworks (BMAD, multi-agent orchestration).

Culture & Benefits

• “Office as an option” with remote work or office choice, plus workation policy.
• Flexible hours and contract forms; comprehensive onboarding with Buddy.
• Unlimited Udemy access, certificate programs, upskilling (500+ certs yearly), knowledge sharing.
• Diverse inclusive community, internal promotions (76% managers), autonomy, well-being activities.
• Modern equipment, charity/environment support, refer-a-friend bonuses.