Nexaminds

80 Total Employees
Year Founded: 2023

Jobs at Nexaminds

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Recently posted jobs

One Month AgoSaved
Remote
Canada
Artificial Intelligence • Cloud • Information Technology • Machine Learning • Natural Language Processing • Consulting
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.
One Month AgoSaved
Remote
Canada
Artificial Intelligence • Cloud • Information Technology • Machine Learning • Natural Language Processing • Consulting
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.
One Month AgoSaved
Remote
2 Locations
Artificial Intelligence • Cloud • Information Technology • Machine Learning • Natural Language Processing • Consulting
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.
One Month AgoSaved
Remote
Canada
Artificial Intelligence • Cloud • Information Technology • Machine Learning • Natural Language Processing • Consulting
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.