Senior Fullstack Data Scientist

Lingaro is a company operating since 2008, with over 1500 talents across 7 global sites. The company offers a flexible "office as an option" work model and comprehensive onboarding with buddy support. It is certified as a Great Place to Work® employer and supports continual learning with access to Udemy and numerous training opportunities.

• 8+ years experience in Data Science/AI engineering.
• At least 4+ years of experience in production-ready Python AI-related code development.
• At least 2+ years experience in production-ready LLM-related code development, preferably based on Retrieval-Augmented Generation (RAG).
• Strong analytical and problem-solving skills 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, 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-managed services.
• Familiarity with CI/CD, testing, and containerized deployments.
• Excellent communication skills in English.

Nice to have:

• Experience designing and programming ML algorithms and data processing pipelines using Python.
• Good understanding of CI/CD and DevOps concepts with tools like GitHub Actions, GitLab, or Azure DevOps.
• Experience in productizing ML solutions using technologies like Spark, Databricks, Docker, Kubernetes.
• Experience with agentic AI development frameworks (e.g., BMAD, multi-agent orchestration, spec-driven AI workflows).

• Lead discovery and solution design for GenAI use cases, including LLM decision, RAGs, fine tuning, agents, and guardrails.
• Build end-to-end GenAI applications covering data ingestion, retrieval layer, orchestration, 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 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 the GenAI landscape and perform targeted PoCs to validate new models and frameworks.