Top Tech Jobs & Startup Jobs in Toronto

Build and operate production search systems using Elasticsearch, Python services, AI inference, embeddings, and LLMs. Responsibilities include index design, relevance tuning, ingestion pipelines, Elastic Cloud operations, Kubernetes deployments, incident response, relevance evaluation, security, CI/CD, monitoring, and API development. The role also involves technical design, documentation, mentoring, and end-to-end ownership of search and AI capabilities.

Build and operate production AI systems using multi-agent workflows, conversational AI, retrieval, structured data, and Python APIs. Own orchestration, state management, evaluation, reliability, cloud deployment, observability, and integration with model and retrieval providers. Establish engineering practices for testing, safety, incident response, and production readiness while balancing quality, latency, and cost.

Build and productionize real-time conversational AI systems using voice, chat, LLM orchestration, retrieval, and multi-channel session management. Own services from architecture through Kubernetes/Azure deployment and operations. Develop evaluation infrastructure, tune LLM pipelines for quality, latency, and cost, integrate AI and voice vendors, and establish engineering practices for a growing team. Partner with product and compliance stakeholders to encode domain rules into reliable AI systems.

Design, build, and operate scalable Generative AI systems and RAG pipelines; own end-to-end AI application lifecycle on Databricks and AKS; integrate LLMs with data sources and APIs; implement LLMOps, evaluation, observability, and production monitoring; mentor engineers and influence product roadmaps.

Lead design, build, and operate scalable Generative AI systems (LLM apps, RAG pipelines, multi-agent systems) in production using Databricks and cloud infrastructure. Own CI/CD, monitoring, evaluation frameworks, optimize retrieval/generation, mentor engineers, and influence product roadmap.