Fullstack Data Scientist - Freelance (Part-time)

Lingaro is a global technology partner operating since 2008 with over 1,600 professionals across 7 global locations. It emphasizes diversity, equity, inclusion, and an ethical business approach ensuring equal opportunities and a respectful workplace.

• Minimum 6 years of experience in Data Science/AI engineering.
• At least 4 years developing production-ready Python AI code.
• At least 2 years developing production-ready LLM-related code, preferably based on Retrieval-Augmented Generation (RAG).
• Strong analytical and problem-solving skills for optimizing AI solutions.
• Deep knowledge and experience with Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
• Understanding of LLM evaluators, validators, and guardrails.
• Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex, LangGraph or similar.
• 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 algorithms, frameworks, model lifecycle, and architectures.
• Proven collaboration skills across technical and non-technical teams.
• Familiarity with cloud environments like Azure (preferred), GCP, or AWS, including AI-related managed services.
• Familiarity with CI/CD, testing, and containerized deployments.
• Excellent communication skills in English.

• Lead discovery and solution design for GenAI use cases, translating business problems into architectures for LLM decision, RAGs, fine-tuning, agents, and guardrails.
• Build end-to-end GenAI applications including data ingestion, retrieval layer, orchestration stacks (e.g., LangChain, LlamaIndex, LangGraph), API/backend, and UI components.
• Design and implement Retrieval-Augmented Generation pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks.
• Perform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for various models including text, code, and multimodal, with evaluation and A/B testing.
• Implement safety, compliance, and governance controls such as input/output filters, PII handling, audit logs, and human-in-the-loop reviews.
• Collaborate with data engineers, product owners, and full-stack developers on architectures, SLAs, and enterprise system integrations.
• Gather technical requirements and estimate planned work.
• Mentor data scientists/engineers on GenAI patterns, code quality, best practices, contributing to internal libraries and reusable components.
• Stay updated on GenAI landscape, conducting PoCs on new models and frameworks.