{"id":2070706,"url":"https://alion.io/job/lingaro-fullstack-data-scientist-freelancer","title":"Fullstack Data Scientist - freelancer","company":{"id":53383,"name":"Lingaro","domain":"lingarogroup.com","url":"https://alion.io/company/lingaro","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":88,"open_postings":29,"ghost_share":0,"stale_share":0.483,"repost_share":0,"time_to_fill_p50_days":41,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Data Science","role_family":"Data 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AI","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"SFT","optional":false},{"name":"Spark","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-08T05:57:45Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T02:25:18Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Tasks:\nLead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine-tuning, agents, guardrails).\n\nBuild end-to-end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed.\n\nDesign and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.\n\nPerform model selection, prompting strategies, and fine-tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing.\n\nImplement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human-in-the-loop review where required).\n\nCollaborate with data engineers, product owners, and full-stack developers on scalable architectures, SLAs, and integration with existing enterprise systems.\n\nGather technical requirements and estimate planned work.\n\nMentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components.\n\nStay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.\n\nWhat We're Looking For:\n6+ years of experience in Data Science/AI engineering.\n\nAt least 4+ years of experience in production-ready Python AI-related code development.\n\nAt least 2+ years of experience in production-ready LLM-related code development, preferably based on the Retrieval-Augmented Generation (RAG) concept.\n\nStrong analytical and problem-solving skills with the ability to optimize AI solutions for diverse applications.\n\nStrong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.\n\nDeep understanding of LLM evaluators, validators, and guardrails.\n\nHands-on experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.\n\nHands-on experience designing or operating MCP servers/clients for LLM agents\n\nStrong Python skills, including production-grade code, packaging, and testing for data/ML services\n\nSolid understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, AI architectures.\n\nProven ability to collaborate effectively across technical and non-technical teams.\n\nFamiliarity with cloud environments such as Azure (preferred), GCP, or AWS, including AI-related managed services.\n\nFamiliarity with CI/CD, testing, and containerized deployments.\n\nExcellent communication skills in English, with the ability to convey complex technical concepts to various audiences.\n\nWhat Will Set You Apart:\nExperience in designing and programming ML algorithms and data processing pipelines using Python.\n\nGood understanding of CI/CD and DevOps concepts, with experience working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps).\n\nExperience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.\n\nExperience with agentic AI development frameworks (e.g., BMAD, multi-agent orchestration, spec-driven AI 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