Platform Engineer

Brampton, Ontario, CanadaHybridContractPosted 2 months ago

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Role summary

We are seeking an Azure Databricks AI Engineer to join our hybrid team in Brampton, ON. This role focuses on building and operationalizing AI/ML platforms, with a strong emphasis on RAG agentic patterns and the Model Context Protocol (MCP). You will leverage Azure Databricks, Unity Catalog, and other Azure services to develop robust AI solutions, manage data governance, and implement CI/CD pipelines. Key responsibilities include designing and building MCP adapters, securing integrations, and ensuring data quality and governance across AI workloads.

Role: Azure Databricks AI Engineer

Location: Brampton, ON- Hybrid

Hybrid

Role Descriptions:

Technical Must Haves

Hands on with Unity Catalog governance and lineage.

Azure services ADLS Gen2| ADF Pipelines| Key Vault| Event Hub Service Bus| Azure DevOps.

Experience delivering RAG agentic patterns (embeddings pipelines| chunking| indexing| evaluation).

Solid SQL and data modeling understanding of medallion and ACID. Proven experience implementing MCP (Model Context Protocol) for agent tool integrations Building MCP servers adapters to expose tools resources (SQL| REST| files| etc.).

Defining tool schemas| input output contracts| and safety guardrails.

Securing MCP integrations with Azure AD Entra| Key Vault| secret scopes| and network controls.

Connecting MCP to Unity Catalog governed assets| Vector Search| and Model Serving.

Essential Skills:

AI GenAI Engineering- MCP Build Gen AIready datasets| embeddings pipelines| and vector indexes for RAG.

Use Databricks Genie to accelerate code creation| optimization| documentation| and refactoring.

Implement tool schemas| resource providers| and prompttool safety policies for MCP.

Integrate MCP with Azure Databricks workloads (Delta tables| Unity Catalog| Vector Search| Model Serving).

Secure MCP endpoints with Key Vault| secret scopes| network rules| and role based access aligned to Unity Catalog.

Operationalize RAGagentic workloads using Vector Search| Model Serving| Feature Store| MLflow.

Azure Cloud Integration -Integrate Azure Databricks with ADLS Gen2| ADF Pipelines| Key Vault| Event Hub Service Azure Functions.

Implement telemetry and observability via Azure Monitor| Log Analytics| and Databricks metrics.

Data Governance -Architecture Implement Unity Catalog for governance| lineage| and access controls across workspaces. Apply Medallion Architecture (Bronze Silver Gold) and enforce data quality with Delta Live Tables and expectations.

Manage PIIPHI controls| data masking| and secure sharing (e.g.| Delta Sharing as applicable).

DevOps Automation- Build CICD pipelines via Azure DevOps or GitHub Actions use Databricks Repos for version control. Orchestrate jobs with Databricks Workflows and or ADF implement promotion paths across environments.

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