GenAI Search Engineer (PL5667)

Paralucent Inc; Toronto; Hybrid; CAD 150,000 - 190,000; Full time

Job summary

Paralucent Inc. in Toronto is seeking an experienced software/ML Engineer to design and deliver secure, scalable enterprise search and knowledge‑retrieval experiences powered by Generative AI.

You will work with product, data, architecture, security and business stakeholders to transform complex information into relevant, grounded answers. You will build production‑grade RAG solutions, develop retrieval pipelines, optimize search relevance, and integrate LLM applications with structured and

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field, or equivalent practical experience.
• Four+ years of software or ML engineering experience in related roles.
• Strong Python programming experience and building scalable APIs/services.
• Hands-on experience delivering LLM or Generative AI solutions.
• Experience with embedding and semantic search, vector databases, and similarity search.
• Experience with Azure AI Search, Elasticsearch/OpenSearch or equivalent search platforms.
• Understanding of APIs, REST, JSON, authentication and distributed architectures.
• Knowledge of working with structured and unstructured data (PDF/Word/HTML/text).
• Strong software engineering practices: Git, automated testing, CI/CD, production troubleshooting.
• Ability to diagnose retrieval failures, hallucinations, and ranking issues.

Responsibilities

• Build production‑grade RAG solutions: design and implement retrieval pipelines for GenAI apps.
• Develop retrieval pipelines: ingestion, parsing, chunking, embedding, retrieval and reranking.
• Improve enterprise search relevance: query rewriting, contextual retrieval and grounding.
• Integrate LLM apps: connect commercial and open‑source LLMs with data sources.
• Build scalable Python APIs and integrations with repos, databases and enterprise systems.
• Evaluate retrieval quality and tune embeddings, prompts and chunking strategies.
• Embed security by design: access controls, privacy, auditable retrieval and responsible AI.
• Support production delivery: architecture/code reviews, testing, CI/CD and observability.

Skills

Python programming

LLM / Generative AI

RAG architectures

APIs / microservices

Vector search / embeddings

Azure AI Search / Elastic

Security by design

CI/CD & testing

Distributed systems

Communication skills

Education

Bachelor’s or Master’s in CS/SE/AI

Tools

Azure AI Search

Elasticsearch/OpenSearch

Vector databases

PDF/Word/HTML data handling

Job description

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.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence or a related discipline, or equivalent practical experience.

• Four or more years of experience in software engineering, search engineering, data engineering, machine learning engineering or related roles.

• Strong Python programming experience, including building scalable APIs and services.

• Hands‑on experience delivering LLM or Generative AI solutions.

• Practical experience designing and building RAG architectures.

• Experience with embeddings, semantic search, vector databases or vector indexes, and similarity search.

• Experience with Azure AI Search, Elasticsearch, OpenSearch or an equivalent search platform.

• Understanding of APIs, REST services, JSON, authentication and distributed application architectures.

• Experience working with structured and unstructured data, including PDF, Word, HTML and text‑based content.

• Strong software engineering practices, including Git, automated testing, CI/CD and production troubleshooting.

• Ability to diagnose retrieval failures, hallucinations, poor ranking, missing context and irrelevant responses.

• Strong communication skills and the ability to translate business needs into scalable technical solutions.

Nice to Have

• Experience with Azure OpenAI, Azure AI services or comparable cloud AI platforms.

• Experience integrating with Microsoft Copilot, Microsoft Graph, SharePoint, Teams or Microsoft 365 content.

• Experience with Pinecone, Weaviate, Milvus, pgvector, FAISS or comparable vector technologies.

• Experience with Semantic Kernel, LangChain, LlamaIndex or similar orchestration frameworks.

• Experience with transformer models, embedding models and reranking models.

• Experience building agentic AI or multi‑agent workflows.

• Knowledge of knowledge graphs, entity extraction, taxonomy or metadata enrichment.

• Familiarity with ML or LLM observability and evaluation tools.

• Experience deploying AI workloads using Azure, Kubernetes, Docker or container‑based platforms.

If you want to do work you’re proud of, with people who own the outcome, this is your place.