Lingaro - Senior Full Stack Data Scientist
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Requirements
• · 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. • · 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).
Responsibilities
• 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.
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