GenAI Search Engineer (PL5667)

Employment Duration: 9 months Location: Toronto, hybrid Employment Type: Full-time About Paralucent Paralucent brings clarity to complex work, helping organizations move beyond complexity toward clarity through practical, thoughtful technology solutions. Paralucent Core Values We’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. Why This Role Matters You 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. Key Responsibilities - Build production-grade RAG solutions: Design and implement Retrieval-Augmented Generation pipelines for enterprise GenAI applications. - Develop retrieval pipelines: Create document ingestion, parsing, chunking, metadata enrichment, embedding, retrieval, reranking and response-generation workflows. - Optimise enterprise search: Implement hybrid keyword, semantic and vector search using Azure AI Search, Elasticsearch, OpenSearch or comparable platforms. - Improve relevance and answer quality: Develop query rewriting, decomposition, contextual retrieval, grounding, citation and multi-step search capabilities. - Integrate LLM applications: Connect commercial and open-source LLMs with structured and unstructured enterprise data sources. - Build scalable services: Develop Python-based APIs and integrations with document repositories, databases, APIs and enterprise systems. - Evaluate and tune solutions: Establish evaluation frameworks, monitor retrieval quality and conduct experiments across embeddings, chunking strategies, rerankers and prompts. - Embed security by design: Ensure document-level access controls, authorisation-aware retrieval, privacy, auditability and responsible AI safeguards. - Support production delivery: Contribute to architecture reviews, code reviews, automated testing, CI/CD, observability and production support.