{"id":1692422,"url":"https://alion.io/job/sx16-ai-engineer-python-llm-rag","title":"AI Engineer - Python / LLM / RAG","company":{"id":2440,"name":"Sx16","domain":"sx16.ai","url":"https://alion.io/company/sx16","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"Claude","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Chroma","optional":true},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"GCP","optional":true},{"name":"Gmail","optional":true},{"name":"Google Drive","optional":true},{"name":"Hybrid Search","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"OneDrive","optional":true},{"name":"Outlook","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Qdrant","optional":true},{"name":"Reranking","optional":true},{"name":"SharePoint","optional":true},{"name":"TypeScript","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-02T11:25:01Z","employer_posted_date":null,"last_verified_at":"2026-10-02T11:25:01Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"We're looking for a hands-on AI Engineer to join Docuverse, an AI-powered document intelligence platform built for consulting, nonprofit, and international development firms. We're looking for someone who enjoys building practical AI applications and production features, rather than working on model training or AI research.\nWe're particularly interested in engineers who can walk us through a real LLM application they have built, explain how their RAG pipeline works, and demonstrate hands-on experience dealing with messy real-world documents. This role is focused on building AI applications on top of existing LLM services. It does not involve model training, fine-tuning, or ML research.\nResponsibilities:\nDevelop and enhance backend features using Python, FastAPI, or Flask.\nBuild and improve RAG/retrieval pipelines that work with external client storage systems through APIs.\nProcess PDF and DOCX documents, including RFPs, TORs, proposals, CVs, project sheets, tables, and scanned content.\nWork with LLM APIs such as Anthropic Claude, OpenAI GPT, and Google Gemini.\nDesign prompts, structured JSON outputs, and section-wise generation workflows.\nBuild AI workflows that provide source-cited outputs.\nDevelop opportunity-screening workflows using public tender/funding databases, email, and other data sources.\nExtract, normalise, match, score, and recommend opportunities using LLM and rule-based workflows.\nIntegrate third-party APIs and external data sources.\nSupport live product pilots, troubleshoot issues, improve output quality, and ship product improvements.\nWrite clean, maintainable, 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