Data Scientist (Generative AI)
- Compensation
- Not disclosed
- Location
- Poland
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Familiarity with theory behind various deep learning concepts.
- Experience with Machine Learning (ML), especially in Generative AI (LLM/LMM) with focus on Natural Language Processing (NLP) or multimodal models.
- Experience with business requirements gathering, transforming them into technical plan, data processing, feature engineering, models evaluation, hypothesis testing and model deployment.
- Fluency in Python and object programing, working knowledge of SQL and vector database.
- Strong tech stack in Azure or GCP cloud.
- Knowledge of specific Deep Learning and GenAI libraries like: PyTorch, HuggingFace, LangChain, LangGraph and GenAI APIs i.e. OpenAI/Gemini.
- Hands-on experience designing or operating MCP servers/clients for LLM agents.
Full description
We are looking for Data Scientist to join Lingaro DS&AI Competency Center, AI team to help our clients deliver value with building end-to-end Talk-To-Data (TTD) Solution powered by LLMs & GenAI models. Lingaro AI team focuses on novel aspects of Generative AI and LLMs with range of applications i.e.: RAG, summarization, multi-agent workflows and model fine tuning. Our deep, PhD level expertise with GenAI, NLP and Computer Vision make us great team to join if you are willing to challenge yourself to new heights. We are looking for a long-term engagement in the space of GenAI and related aspects.
Tasks:
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Build end-to-end GenAI applications such as chatbots, voicebots, Talk-to-Data systems, where you will help buiild: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed.
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Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.
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Perform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including guardrails, output evaluation and A/B testing.
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Designing, integrating and optimizing LLMs interaction with external tools, APIs, and data sources in a standardized and scalable way by using Model Context Protocol (MCP) connectors.
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Business understanding and translating problems into technical goals by defining success metrics, auditing data feasibility, and aligning stakeholder expectations.
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Supporting project delivery.
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Supporting Pre-sales activities and initiatives.
What We're Looking For:
-
Familiarity with theory behind various deep learning concepts.
-
Experience with Machine Learning (ML), especially in Generative AI (LLM/LMM) with focus on Natural Language Processing (NLP) or multimodal models.
-
Experience with business requirements gathering, transforming them into technical plan, data processing, feature engineering, models evaluation, hypothesis testing and model deployment.
-
Fluency in Python and object programing, working knowledge of SQL and vector database.
-
Strong tech stack in Azure or GCP cloud.
-
Knowledge of specific Deep Learning and GenAI libraries like: PyTorch, HuggingFace, LangChain, LangGraph and GenAI APIs i.e. OpenAI/Gemini.