Fullstack Data Scientist - Freelance (Part-time)

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SeniorZdalnie·WarszawaPart-time

#479442·Dodano 2 dni temu·2

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Data scienceGenerative AI

Podsumowanie oferty

Lead discovery and solution design for GenAI use cases and build end-to-end GenAI applications, including LLM and RAG solutions. The work involves Python and orchestration frameworks such as LangChain, LlamaIndex, and LangGraph.

Wymagania

6+ years of experience in Data Science/AI engineering.
At least 4+ years of experience developing production-ready Python code related to AI.
At least 2+ years of experience developing production-ready code related to LLMs, preferably based on the Retrieval-Augmented Generation (RAG) concept.
Strong analytical and problem-solving skills, with the ability to optimize AI solutions for diverse applications.
Strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
Deep understanding of LLM evaluators, validators, and guardrails.
Hands-on experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.
Hands-on experience designing or operating MCP servers/clients for LLM agents.
Strong Python skills, including production-grade code, packaging, and testing for data/ML services.
Solid understanding of ML/AI concepts, including algorithm types, machine learning frameworks, model efficiency metrics, model lifecycle, and AI architectures.
Proven ability to collaborate effectively across technical and non-technical teams.
Familiarity with cloud environments such as Azure (preferred), GCP, or AWS, including AI-related managed services.
Familiarity with CI/CD, testing, and containerized deployments.
Excellent communication skills in English, with the ability to convey complex technical concepts to various audiences.

Obowiązki

Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures involving LLM decisions, RAG, fine-tuning, agents, and guardrails.
Build end-to-end GenAI applications covering data ingestion, retrieval, orchestration (e.g. LangChain, LlamaIndex, LangGraph), APIs/backend, and a simple UI where needed.
Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.
Perform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing.
Implement safety, compliance, and governance controls, including input/output filters, PII handling, audit logs, and human-in-the-loop review where required.
Collaborate with data engineers, product owners, and full-stack developers on scalable architectures, SLAs, and integration with existing enterprise systems.
Gather technical requirements and estimate planned work.
Mentor other data scientists and engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components.
Stay current with the GenAI landscape, including new open and hosted models, agentic frameworks, and evaluation techniques, and perform targeted PoCs to validate them.

Benefity

Flexible collaboration model and working hours.
Autonomy to choose the way you work.

Elastyczne godziny

Lingaro

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