Senior Fullstack Data Scientist

Lingaro is an established company since 2008 with over 1500 talents across 7 global sites.

• 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 Retrieval-Augmented Generation (RAG).
• Strong analytical and problem-solving skills with the ability to optimize AI solutions.
• 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 GenAI frameworks such as 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: algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, and AI architectures.
• Proven ability to collaborate 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.

• Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures involving LLM, RAGs, fine tuning, agents, and guardrails.
• Build end-to-end GenAI applications including data ingestion, retrieval layer, orchestration (e.g., LangChain, LlamaIndex, LangGraph), API/backend, and simple UI.
• Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks.
• 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 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 scalable architectures, SLAs, and enterprise system integration.
• 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 GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform PoCs to validate them.