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AI Data EngineerBmo • Chicago, Illinois, United States
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AI Data Engineer

Bmo
  • US
    Chicago, Illinois, United States
  • US
    Chicago, Illinois, United States
Jetzt Bewerben

Über

Hybrid role (2 days/week in the office)
Team Overview We accelerate BMO’s AI journey by building enterprise-grade, cloud-native AI solutions. Our team combines engineering excellence with cutting‑edge AI to deliver scalable, secure, and responsible solutions that power business innovation across the bank. We enable and accelerate our partners on their AI journeys across the enterprise, helping teams across BMO unlock value at scale. We are engineers, AI practitioners, platform builders, thought leaders, multipliers, and coders. Above all, we are a global team of diverse individuals who enjoy working together to create smart, secure, and scalable solutions that make an impact across the enterprise.
Our ambition is bold: deploy our capital and resources to their highest and most profitable use through a digital‑first operating model, powered by data and AI‑driven decisions.
Key Responsibilities
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.
Required Qualifications
(1‑3 years) Practical experience building and deploying production grade data pipelines including but not limited to (text, images, pdf documents, structured and unstructured data).
(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) data pipelines.
(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.
Salary $81,400.00 - $151,800.00
The above represents BMO Financial Group’s pay range and type.
Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part‑time roles will be pro‑rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.
BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance‑based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards
Equal Employment Opportunity BMO is proud to be an equal employment opportunity employer. We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics. We also consider applicants with criminal histories, consistent with applicable federal, state and local law.
BMO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e‑mail to BMOCareers.Support@bmo.com and let us know the nature of your request and your contact information.
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  • Chicago, Illinois, United States

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  • English
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