{"id":2919317,"url":"https://alion.io/job/craftware-data-scientist-rag-document-intelligence-2","title":"Data Scientist – RAG & Document Intelligence","company":{"id":4184851,"name":"Craftware","domain":"craftware.com","url":"https://alion.io/company/craftware-3","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_board_market","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":98000,"period":"year","method":"role_country_seniority_unknown","sample_n":367},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"DeepEval","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"UiPath","optional":false},{"name":"Weaviate","optional":false},{"name":"FastAPI","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-20T10:47:22Z","employer_posted_date":null,"last_verified_at":"2026-10-20T10:47:22Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Craftware is a technology company of over 500 experts, empowering large organizations to solve complex business challenges with modern IT solutions - from sales systems and automation to data platforms and AI. We operate where technology must be reliable, secure, and scalable. We deliver end-to-end projects: from analysis and architecture through implementation to development and maintenance. We are a trusted partner of industry leaders such as Salesforce, Veeva, UiPath, and Databricks.\nModel: remote\nEngagement: full-time\nRole summary\nYou’ll join one of the most exciting AI initiatives at an international Consumer Health company: a platform that lets business users retrieve, synthesize, and act on knowledge locked inside complex enterprise documents, using plain language. This is a greenfield, research-meets-engineering role. You’ll design, experiment with, and implement a context-aware, multi-agent AI system, with real influence over model selection, retrieval strategies, and quality standards. Accuracy is the product here: you’ll own the quality bar and have the mandate to keep raising it for business users across commercial, marketing, product supply, and R&D.\nResponsibilities\nOptimize RAG pipelines through experimentation: systematically test parsing and chunking strategies, embedding models, retrieval algorithms, re-ranking approaches, and context assembly techniques to maximize answer quality and minimize hallucination.\n\nDrive document intelligence: evaluate and benchmark document parsing solutions for PDFs, Word documents, presentations, scanned files, and mixed-format corpora, and extract structured meaning from noisy inputs, including tables, charts, and figures.\n\nEvaluate and compare models: design and run rigorous experiments comparing LLMs, embedding models, and retrieval models across accuracy, latency, reliability, and cost. Build evaluation datasets and define RAG-specific quality metrics.\n\nApply NLP and prompt engineering: work on semantic similarity, named entity recognition, query rewriting, and multi-turn conversation handling. Craft, version, and systematically evaluate prompts and prompt chains.\n\nBalance quality and cost: track token consumption, latency, and retrieval quality across experiments, and make principled trade-offs between model capability and operational cost at enterprise scale.\n\nRequirements\nMust-have:\nStrong Python skills with hands-on experience in machine learning and generative AI workflows, including production-grade code\n\nSolid NLP background: text representation, semantic search, embeddings, and language model behaviour\n\nDeep hands-on experience designing and optimizing RAG pipelines over unstructured documents: chunking strategies, embedding models, hybrid search, re-ranking (e.g. LightRAG, LlamaIndex, LangChain)\n\nExperience with document parsing and noisy, multimodal document formats, including extracting information from tables, charts, and figures\n\nExperience with LLM evaluation frameworks and RAG-specific quality metrics (e.g. RAGAS, DeepEval, Langfuse)\n\nFamiliarity with multi-agent AI frameworks such as LangGraph, LangChain, or Pydantic AI\n\nExperience with vector databases (e.g. pgvector, Pinecone, Qdrant, Weaviate)\n\nExperience with Azure cloud services (Azure Apps, Containers, Storage, AI Search, AI Foundry) and/or AWS equivalents\n\nExperience with Databricks (Delta Lake, Unity Catalog, MLflow)\n\nStrong analytical mindset with a structured, experiment-driven approach to problem solving\n\nFluent English, both written and spoken\n\nNice-to-have:\nHands-on experience with Databricks GenAI products: Vector Search, Agent Framework, Knowledge Assistance, Genie, Agent Bricks\n\nAPI development and integration skills (e.g. FastAPI)\n\nKnowledge of Model Context Protocol (MCP) for tool integration\n\nUnderstanding of knowledge management, taxonomy design, and metadata enrichment for enterprise document repositories\n\nWe offer:\nB2B contract (rate negotiated individually, up to 200 PLN net/h + VAT)\n\nFully remote service delivery\n\nBroad range of projects (internal, international) - genuine variety of clients and tasks\n\nBudget for skills development and certifications as part of the collaboration\n\nRegular collaboration reviews and discussion of project scope\n\nAdditional benefits available as part of the collaboration\n\nNetworking and team-building events for project teams\n\nTech stack\nPython\nRAG\nNLP\nLLM\nDataLakes\nAzure Apps","description_format":"text","description_chars":4427,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Poland","iso":"PL","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Enterprise Apps Implementation Partners"],"lifecycle":[{"event":"open","at":"2026-10-20T11:25:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.902,"p_room":1,"age_days":4,"expected_fill_days":27,"reasons":["seen:4","velocity","win:early"],"computed_at":"2026-10-25T05:49:26Z"},"pay":null,"html_url":"https://alion.io/job/craftware-data-scientist-rag-document-intelligence-2","json_url":"https://alion.io/job/craftware-data-scientist-rag-document-intelligence-2.json","meta":{"generated_at":"2026-10-25T14:49:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":21365,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-26T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4184851},"rest":"https://alion.io/mcp/rest/get_company?id=4184851"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcraftware-data-scientist-rag-document-intelligence-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcraftware-data-scientist-rag-document-intelligence-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcraftware-data-scientist-rag-document-intelligence-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/craftware-data-scientist-rag-document-intelligence-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcraftware-data-scientist-rag-document-intelligence-2"}]}