Fullstack Data Scientist

New

LingaroGenerative AI

PolandContractSenior

Salary not disclosed

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Job Details

Languages: Excellent communication skills in English
Experience: 6+ years of experience in Data Science/AI engineering; at least 4+ years of experience in production-ready Python AI-related code development; at least 2+ years of experience in production-ready LLM-related code development
Required Skills: PythonAzureCI/CDGenerative AILangChain

Have 6+ years of experience in Data Science/AI engineering.
Have at least 4+ years of experience developing production-ready Python AI-related code.
Have at least 2+ years of experience developing production-ready LLM-related code, preferably using RAG.
Have strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
Understand LLM evaluators, validators, and guardrails.
Have hands-on experience with LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.
Have hands-on experience designing or operating MCP servers or clients for LLM agents.
Have strong Python skills, including production-grade code, packaging, and testing for data/ML services.
Understand ML/AI concepts, including algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, and AI architectures.
Be familiar with cloud environments such as Azure, GCP, or AWS, including AI-related managed services.
Be familiar with CI/CD, testing, and containerized deployments.
Have excellent English communication skills and the ability to convey complex technical concepts to varied audiences.

Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures.
Build end-to-end GenAI applications covering data ingestion, retrieval, orchestration, APIs or backends, and simple user interfaces where needed.
Design and implement RAG pipelines using vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks.
Select models, develop prompting strategies, and fine-tune text, code, and multimodal models; evaluate results and run A/B tests.
Implement safety, compliance, and governance controls, including filters, PII handling, audit logs, and human review where required.
Collaborate with data engineers, product owners, and full-stack developers on scalable architectures, SLAs, and enterprise integrations.
Gather technical requirements and estimate planned work.
Mentor data scientists and engineers, and contribute to internal libraries, templates, and reusable components.
Evaluate emerging GenAI models, agentic frameworks, and evaluation techniques through targeted proofs of concept.

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