Fullstack Data Scientist - freelancer

Job description

### Tasks: - Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine-tuning, agents, guardrails). - Build end-to-end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and 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 (input/output filters, PII handling, audit logs, 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/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components. - Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them. ### ### What We're Looking For: - 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, 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: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, 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. ### What Will Set You Apart: - Experience in designing and programming ML algorithms and data processing pipelines using Python. - Good understanding of CI/CD and DevOps concepts, with experience working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps). - Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes. - Experience with agentic AI development frameworks (e.g., BMAD, multi-agent orchestration, spec-driven AI workflows).