AI Data Engineer

BMO U.S.

AI Data Engineer developing enterprise-grade, cloud-native AI solutions for BMO. Collaborating across teams to accelerate AI journeys and deliver scalable, secure solutions.

Posted 6/17/2026full-timeVirtual • Missouri • 🇺🇸 United StatesJunior💰 $81,400 - $151,800 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudETLGoogle Cloud PlatformPythonTerraform

About the role

Key responsibilities & impact

Design and implement reliable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured data (e.g., databases, files, documents, APIs, events), and multi-modal data, ensuring data is AI ready, governed, secure, and observable
Experience applying data quality, validation, monitoring and testing frameworks in production pipelines
Ensure pipelines follow enterprise governance, access control, and security standards, including role-based access and lineage considerations
Monitor pipeline performance, troubleshoot failures, and optimize cost and throughput
Integrate AI services (e.g., document processing/understanding, content understanding, embeddings, search, LLM APIs) into production data workflows
Build, automate and maintain ETL/ELT pipelines using cloud-native services and distributed processing frameworks
Develop production-grade services using Python and REST APIs to expose data and AI capabilities
Partner with leadership to clarify expected outcomes/vision and translate them into an executable build plan, architecture decisions, and delivery milestones
Develop feature engineering pipelines to support ML and GenAI use cases, including retrieval-augmented generation (RAG)
Own the development of AI data engineering standards, best practices, and reusable frameworks, driving consistency and quality across teams and platforms
Lead collaboration with cross-functional teams to ensure clear, consistent definition and alignment of data input and output requirements.

Requirements

What you’ll need

(1-3 years) Practical experience building and deploying production grade data pipelines
(3-5 years) Experience sourcing data from storage services (AWS S3, Azure Blob), API’s, Data warehouses, Data lakes, and file systems
(1-3 years) Experience building feature stores, and data marts for ML applications
(3-5 years) Experience in development of foundational Data lineage tracking and ensuring data quality
(3-5 years) Experience in data governance best practices – such as PII detection, data masking, and role-based access
(3-5 years) Experience with Gen AI use cases – LLM’s, RAG (Retrieval-Augmented generation), embeddings, chunking strategies
MUST HAVE TECH STACK– Python, Azure Data Factory, Azure Functions, Prompt engineering, Cosmos DB
Nice to have: (1-3 years) Practical experience building and deploying production grade multi-modal data pipelines (Audio and Video files)
(1-2 years) Leadership – Focus on reusable components, architecture, and design patterns. Emphasis on cost optimization techniques
(1-2 years) Leadership - Experience working with cross-functional teams
Concurrent development using Git and other source control tools, and CI/CD pipelines
Experience across Azure, AWS, and GCP
Experience with Synthetic data generation.
Experience with multi-Agent setup and orchestration.
Tech skills – Familiarity with Terraform, Cloud formation, Bicep, Azure fabric.

Benefits

Comp & perks

Health insurance
Tuition reimbursement
Accident and life insurance
Retirement savings plans
Performance-based incentives
Discretionary bonuses

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data ingestiondata integrationETLELTPythonREST APIsfeature engineeringdata qualitydata governanceGen AI

Soft Skills

leadershipcollaborationcommunicationproblem-solvingcost optimization