{"id":3099440,"url":"https://alion.io/job/paralucent-genai-search-engineer-pl5667","title":"GenAI Search Engineer (PL5667)","company":{"id":685853,"name":"Paralucent","domain":"paralucent.com","url":"https://alion.io/company/paralucent","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"C","score":61,"open_postings":23,"ghost_share":0.652,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":65000,"max_usd":147000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenSearch","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"AI Agents","optional":true},{"name":"Copilot","optional":true},{"name":"Docker","optional":true},{"name":"FAISS","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Milvus","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenAI","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"SharePoint","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-22T15:51:25Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-11-06T04:47:27Z","board_verified":true,"closed_at":null,"days_open":14,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":13},"description":"Employment Duration: 9 months\nLocation: Toronto, hybrid\nEmployment Type: Full-time\nAbout Paralucent\nParalucent brings clarity to complex work, helping organizations move beyond complexity toward clarity through practical, thoughtful technology solutions.\nParalucent Core Values\nWe’re Driven to deliver real outcomes, and we Take Ownership from first question to final result. We’re Creative Problem Solvers who cut through complexity to bring clarity, and we’re Humbly Confident (high standards, low ego, always learning). Above all, we’re Customer Centric: we build with empathy, partner closely, and measure success by the impact we create for the people who rely on what we deliver.\nWhy This Role Matters\nYou will design and deliver secure, scalable enterprise search and knowledge-retrieval experiences powered by Generative AI. Working closely with product, data, architecture, security and business stakeholders, you will turn complex enterprise information into relevant, grounded, traceable and useful answers.\nKey Responsibilities\nBuild production-grade RAG solutions: Design and implement Retrieval-Augmented Generation pipelines for enterprise GenAI applications.\n\nDevelop retrieval pipelines: Create document ingestion, parsing, chunking, metadata enrichment, embedding, retrieval, reranking and response-generation workflows.\n\nOptimise enterprise search: Implement hybrid keyword, semantic and vector search using Azure AI Search, Elasticsearch, OpenSearch or comparable platforms.\n\nImprove relevance and answer quality: Develop query rewriting, decomposition, contextual retrieval, grounding, citation and multi-step search capabilities.\n\nIntegrate LLM applications: Connect commercial and open-source LLMs with structured and unstructured enterprise data sources.\n\nBuild scalable services: Develop Python-based APIs and integrations with document repositories, databases, APIs and enterprise systems.\n\nEvaluate and tune solutions: Establish evaluation frameworks, monitor retrieval quality and conduct experiments across embeddings, chunking strategies, rerankers and prompts.\n\nEmbed security by design: Ensure document-level access controls, authorisation-aware retrieval, privacy, auditability and responsible AI safeguards.\n\nSupport production delivery: Contribute to architecture reviews, code reviews, automated testing, CI/CD, observability and production support.\n\nRequirements\nQualifications\nBachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence or a related discipline, or equivalent practical experience.\n\nFour or more years of experience in software engineering, search engineering, data engineering, machine learning engineering or related roles.\n\nStrong Python programming experience, including building scalable APIs and services.\n\nHands-on experience delivering LLM or Generative AI solutions.\n\nPractical experience designing and building RAG architectures.\n\nExperience with embeddings, semantic search, vector databases or vector indexes, and similarity search.\n\nExperience with Azure AI Search, Elasticsearch, OpenSearch or an equivalent search platform.\n\nUnderstanding of APIs, REST services, JSON, authentication and distributed application architectures.\n\nExperience working with structured and unstructured data, including PDF, Word, HTML and text-based content.\n\nStrong software engineering practices, including Git, automated testing, CI/CD and production troubleshooting.\n\nAbility to diagnose retrieval failures, hallucinations, poor ranking, missing context and irrelevant responses.\n\nStrong communication skills and the ability to translate business needs into scalable technical solutions.\n\nNice to Have\nExperience with Azure OpenAI, Azure AI services or comparable cloud AI 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