{"id":2272113,"url":"https://alion.io/job/microtech-software-experienced-ai-engineer","title":"Experienced AI Engineer","company":{"id":4404723,"name":"MicroTech Software","domain":"microtech.dk","url":"https://alion.io/company/microtech-software","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":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Aarhus, Denmark"],"countries":["DK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AutoGen","optional":false},{"name":"Chroma","optional":false},{"name":"Clean Architecture","optional":false},{"name":"CrewAI","optional":false},{"name":"Deepgram","optional":false},{"name":"Edge AI","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"llama.cpp","optional":false},{"name":"LLM","optional":false},{"name":"NLP","optional":false},{"name":"Ollama","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Whisper","optional":false},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"EU AI Act","optional":true},{"name":"GDPR","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-08T22:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-08T22:00:00Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"About the position\n\nJoin MicroTech Software to build cutting-edge AI-powered tools for insurance professionals. Work on production RAG chatbots, agentic workflows, and voice-to-workflow capabilities.\n\nAt MicroTech Software we build cutting edge AI-powered tools that help insurance professionals answer complex questions faster and with greater confidence. Our flagship product is a production RAG chatbot used daily by insurance workers, and we are actively pushing its architecture in several new directions at once using the newest models. Your reference will be our AI Lead. We are looking for an engineer with 2–4 years of experience who has reached the point where writing good code is no longer the hard part — and who is ready to take on the harder questions: which approach to use, how to structure a system for long-term maintainability, and where to invest engineering time to get the most impact. You will work in a team with a very advanced team lead whom you can expect to learn from and alongside one or more junior engineers and be expected to help set the direction of their work as well as your own.\n\nResponsibilities\n\nShape the architecture of a live AI product\n\nEvaluate and challenge design decisions across the stack — retrieval strategies, LLM backend selection, service boundaries, data flow — and propose improvements with clear reasoning\n\nParticipate in technical planning with the team and with insurance-domain stakeholders, translating vague requirements into concrete engineering tasks\n\nIdentify technical debt, assess its cost, and make the case for addressing it\n\nNavigate an on-prem transition\n\nDesign and lead the migration of a cloud-dependent LLM stack to a fully local alternative: self-hosted models, local embedding, and on-prem vector stores\n\nEvaluate open-weight LLMs for Danish-language RAG quality; understand the trade-offs in latency, accuracy, and hardware requirements\n\nContribute to a RAG → GraphRAG transition: build a knowledge graph and integrate it into an existing RAG pipeline\n\nDesign and build agentic workflows\n\nDesign multi-step agent architectures using LangGraph or equivalent frameworks that can route, plan, use tools, and synthesize across document corpora\n\nDefine tool-use interfaces, memory patterns, and failure handling strategies for agent loops running in production\n\nWork with domain experts to identify automation opportunities worth building\n\nEnable voice-to-workflow capabilities\n\nContribute to the design of a pipeline that integrates local speech-to-text models into agentic workflows\n\nEvaluate Danish-language ASR quality, identify gaps, and assess the feasibility of fine-tuning or model selection approaches\n\nDesign the integration points carefully — latency, error handling, data privacy — so voice becomes a first-class input channel\n\nWe are looking for someone who can say yes to\n\nMust-have\n\n2–4 years of production Python engineering experience, including async code, clean architecture patterns, and testable codebases\n\nDirect experience with LLM orchestration frameworks (LangChain or similar) in a shipped product or serious project\n\nSolid understanding of RAG systems: chunking strategies, embedding models, vector search, retrieval quality evaluation\n\nFamiliarity with vector databases — pgvector, Chroma, Weaviate, Pinecone, or equivalent\n\nComfortable owning a FastAPI service end-to-end: schema design, error handling, auth, containerization, deployment\n\nAbility to reason about architectural trade-offs and communicate them clearly\n\nStrong advantages\n\nHands-on experience designing or running local LLM inference — Ollama, vLLM, llama.cpp — in a real rather than toy context\n\nFamiliarity with knowledge graphs — ontology building, knowledge extraction, RAG integration\n\nExperience with agentic frameworks: LangGraph, AutoGen, CrewAI, or custom tool-use / planning loops in production\n\nSpeech-to-text pipeline work — Whisper, Deepgram, Speechmatics, or comparable local STT — especially on non-English languages\n\nExperience designing hybrid retrieval systems (dense + sparse, BM25 + vector) and re-ranking with cross-encoders\n\nFamiliarity with LLM observability and tracing tools (Arize Phoenix, LangSmith, or similar)\n\nExposure to multilingual NLP, particularly Danish or other Nordic languages\n\nNice to have\n\nKnowledge of regulated-industry data handling, GDPR, or EU AI Act compliance considerations\n\nFamiliarity with cloud platforms (Azure preferred): managed services, container registries, CI/CD\n\nInterested?\n\nSend your application via our online form or directly to us.\n\nOr send to  · Call us at +45 7585 9444 for questions\n\nWe offer\n\n• Beautiful offices at Viby J. station\n\n• Free parking\n\n• Opportunity to work hybrid\n\n• Competitive salary\n\n• Short decision-making process\n\n• Personal 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