Top Tech Jobs & Startup Jobs in Montreal

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, establishes CI/CD quality gates, drives ETL and data validation, and governs responsible AI adoption in QA. The role leads QA transformation initiatives, mentors engineers, collaborates with Engineering, DevOps, Data, and Product teams, and evaluates AI-enabled testing capabilities.

Design and lead scalable data engineering solutions using Databricks and Azure. Establish enterprise data modeling and engineering standards, architect pipelines and integrations, improve platform reliability and performance, guide modernization and cloud migrations, troubleshoot complex issues, and mentor data engineers while ensuring security, quality, and governance.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.

Design, build, optimize, and maintain scalable Databricks data pipelines and ETL/ELT workflows. Manage CI/CD with Azure Pipelines and Azure Key Vault, improve performance and cost efficiency, implement data quality and governance practices, troubleshoot distributed systems, collaborate with stakeholders, and support cloud data platform modernization initiatives.

Design, build, and deploy production-grade AI applications, integrations, pipelines, and agentic workflows for customers. Lead discovery, prototyping, architecture workshops, technical troubleshooting, and customer go-lives. Integrate LLMs, retrieval systems, tools, and orchestration into enterprise environments, establish reusable engineering patterns, mentor customer-facing engineers, and translate customer feedback into product and engineering priorities.

Design, build, deploy, and support scalable backend APIs and agentic AI solutions using Go. Architect microservices and cloud infrastructure (Azure/GCP), integrate data platforms, create API docs and partner enablement assets, and ensure operational readiness with CI/CD, SLOs, and security-by-design.

Lead AI validation strategy for LLMs, RAG, and agent systems. Build automated CI/CD validation pipelines, design semantic and prompt regression tests, implement guardrails and confidence scoring, monitor production AI quality, and partner with engineering and data governance to ensure safe, accurate, production-ready AI.