Job Type

Continuing

Close Date

11/30/2025

Employee Group

TMG

Favorite Job

Department

UTS Applications & Data System

Salary Grade/Band

Band M

Salary Range

$101466.00 - $152200.00 (annual)

Hours per Week

35

Posting Details

Schedule

Monday - Friday, 8:30 am - 4:30 pm

Education Level

Bachelor's degree in Computer Sciences or Software Engineering or a related field of study

Career Level

8 - 10 years of technical experience

Job Description

At McMaster University, we strive to attract, develop, and retain talented faculty and staff, and to foster inclusive excellence which values the strengths, perspectives, and contributions of each individual. McMaster is one of the Top 70 Universities in the World and is recognized as Canada’s Most Research-Intensive University. McMaster continues to be recognized as one of the top employers in the Hamilton/Niagara region and one of Canada’s Top Diversity employers.

Our University Technology Services UTS) team mission is to provide exceptional customer service and a high level of support to the McMaster community. Critical to the success of this role we stand by and value integrity, mutual respect, collaboration and cooperation in support of the University’s IT Strategic Vision of a connected One IT community.

About the Position

The Data and Machine Learning Engineer is a senior technical specialist responsible for building and scaling the university's production-grade AI capabilities. This role requires deep expertise at the intersection of advanced software engineering, robust data engineering, and machine learning.

The role involves end-to-end ownership, starting with designing and optimizing robust data pipelines (ETL/ELT) within the university’s existing data platforms to deliver high-quality, analytics- and features-ready data, and extending through the development, deployment, and operationalization (MLOps) of production-grade machine learning models. The Engineer drives technical programming standards, leverages cloud-native services (Azure), and ensures the entire system, from data lake to deployed AI model, is reliable, compliant, and performs at scale.

This role provides the essential technical bridge between raw data sources and actionable predictive intelligence, supporting the university's data modernization and informed decision-making goals.

Specific Accountabilities For This Role Include:

• Machine Learning and AI Engineering: Design, develop, and implement scalable machine learning models and AI solutions from proof-of-concept to production. Apply expertise in Python and relevant ML/AI libraries (e.g., scikit-learn, TensorFlow, PyTorch) to solve complex university challenges.
• Data Engineering for AI (Collaborative): Execute the build-out of data pipelines and processes specifically required for ML/AI solutions. This includes feature engineering and data preparation, ensuring alignment with the data platform design and governance standards.
• Data Modeling Support: Implement and maintain data structures and assets required for ML/AI, working within the overall conceptual, logical, and physical data models.
• Privacy, Security, and Ethical AI: Establish and enforce security-by-design principles across all data and model workflows. Implement appropriate controls (e.g., encryption, tokenization, anonymization) for sensitive data. Ensure all AI/ML models are developed, deployed, and monitored in compliance with PIPEDA, institutional data governance policies, and responsible AI frameworks (including fairness, transparency, and bias detection).
• Advanced Technical Programming: Write and maintain robust, production-ready systems in Python (leveraging advanced software engineering patterns) and secure REST APIs (e.g., with FastAPI) for seamless data ingestion and model serving.
• Database Utilization: Utilize relational databases (Oracle, MS SQL Server) for ML/AI data access.
• MLOps and Model Management: Implement and manage MLOps practices, including version control, continuous integration/continuous deployment (CI/CD) for models, and monitoring of model performance in production.
• Technical Collaboration and Execution: Act as the subject matter expert for ML/AI within the team, providing specialized execution and technical detail.

Are you the right candidate?

You bring 8–10 years of progressive technical experience, including technical execution in data engineering, project management and software deployment. You have demonstrated, hands-on knowledge and experience with machine learning and artificial intelligence, including developing and deploying models in production environment. You are a key technical implementer, able to translate direction into functional, scalable ML/AI systems.

With exceptional communication and interpersonal skills, you thrive in collaborative environments and excel at aligning diverse stakeholder needs to deliver effective, structured solutions. You have a strong understanding of relational databases, security systems, transaction processing, and web-based technologies, as well as a deep knowledge of project management principles and the full project lifecycle.

Your expertise includes data modeling, system architecture, and BI/analytics platforms, backed by over a decade of hands-on experience in complex organizational settings. You’ve worked extensively with diverse data platforms, ETL automation, dimensional modeling, APIs (JSON, XML), Power BI, and DevOps tools like GitHub and Azure DevOps. Proficient in Azure Data Lake, MS Fabric, Data Factory, and MS Purview, you are also experienced with data modelling tools, e.g. Erwin Data Modeler, and agile methodologies. Experience with university systems and a demonstrated ability to lead teams round out your qualifications, making you well-equipped to take on a leadership role in data engineering.

Key Responsibilities:

Deliver project technology

• Ensure the technical components of the projects are delivered into a properly managed and controlled production environment using project life cycle standards.

Manage a high performance team

• Create and manage a high performance technical team accountable for application implementation, data conversion, interface development, testing, change and version management, security and privacy among other matters.

Define system architectures

• Research, define and document the data and system physical and logical architectures. Ensure project team has a shared understanding of all data sources, data flows, data model, system components and interfaces. Conduct project walkthroughs. Review all processes and work with clients on changes required to work flow, business processes or business rules.

Manage system testing

• Develop and manage a comprehensive test plan. Ensure that unit, system and business processes are tested prior to implementation and that any financial systems are consistent with financial management principles and CICA control guidelines. Ensure that appropriate test and acceptance processes are in place for all customizations (unit, system, volume and stress testing), as appropriate.

Manage data conversion

• Understand, define, document and manage the data conversion process to ensure that source data is correct, that standards are consistent, and that appropriate controls are in place to validate conversion. Develop a conversion plan that meets business needs and possible policy changes.

Manage system interfaces

• Understand, define, document and manage the development of complex interfaces with existing applications including the planned migration to a service-oriented architecture.

Procure hardware and software

• Working with Purchasing and Software Supplier, procure necessary hardware & software. Manage configuration of the hardware required to create a test, development and production environment. In consultation with vendors, install and manage the configuration of the test, development and production databases. In consultation with Software Supplier, install and configure the Application.

Enforce privacy and confidentiality controls

• Ensure that appropriate controls are in place to protect the privacy of personal and confidential data in accordance with PIPEDA and other legislation and policies.

Define new business processes

• Many of the projects will be business transformation initiatives and the incumbent may need to assist with and where necessary lead stakeholders to define new business processes, new workflow and new business rules to optimize client service, achieve policy and legislative compliance, reflect good financial management and audit principles, and achieve business objectives. Develop a comprehensive security plan in consultation with the Security Officer to ensure that roles are appropriately defined, separation of duties occurs, financial controls are in place and that the solution is secure and reliable.

Develop staff skills

• Coach development staff and develop a comprehensive technology transfer program to ensure that maintenance staff can sustain the operation of the system subsequent to implementation with minimal assistance from Software Supplier.

• Understand and document all system administration functions (i.e. configuration parameters, updates, version and patch management, implementation of customized work orders, year end rollover, accruals, and policy and legislative compliance).

• Ensure that appropriate communication and controls are in place for all changes in a production environment. Ensure systematic decommissioning of expired systems with no impact to current & future operations.

Manage project components

• Identify and select development strategies to maximize the efficiency and effectiveness of project work while minimizing negative impacts on stakeholders.

• Apply specialized knowledge to ensure proper setting of priorities, problem resolution and incorporation of changing events and conditions into the project.

• Ensure that appropriate project information is communicated effectively and regularly to all stakeholders. Present project issues as needed in stakeholder meetings and suggest recommendations for resolution.

• Ensure that project application is effectively integrated into current systems and, where possible, any current production problems are addressed.

Skills and Qualifications

• Bachelor’s degree in Computer Sciences or Software Engineering or a related field of study

• The incumbent should have 8 – 10 years of technical experience with increasing levels of responsibility in data engineering, project management and software package deployment

• Exceptional leadership skills

• Proven ability to design, implement, and deploy end-to-end machine learning models in a production environment, including proficiency in model serialization and deployment via REST APIs (e.g., using FastAPI or Flask).

• Deep expertise in MLOps practices: model versioning (e.g., MLflow, DVC), monitoring (e.g., Azure Application Insights, Prometheus), drift detection, and automated deployment (CI/CD) using Docker and Kubernetes/Azure Kubernetes Service (AKS).

• Superior communication and interpersonal skills

• A proven ability to achieve goals through collaboration while working as part of a larger team of diverse stakeholders

• The incumbent must demonstrate knowledge and ability to synthesize the results of business processing analysis, systems analysis and technical analysis into realistic structured work plans

• Experience with and understanding of relational database management systems, security systems, batch and online transaction processing and web presentation in a production environment is required

• Excellent knowledge of all aspects of project management and project lifecycle

• Strong knowledge of web deployment and transaction driven database interactions

• Expert knowledge of data modeling, system architectures, relational database (Oracle) and package implementation

• Knowledge of university systems and policies

• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

• Knowledge and work experience with Industry Data Models

• 10+ years of experience in a formal data modelling role in a complex organization

• 7+ years of experience working with BI/Analytics platforms including working with data management solutions, master data management, data quality, and data warehousing implementation.

• Extensive experience working with data lakes, and data platforms supporting self-service analytics, application development and integrations.Must have demonstrated working experience with:

• Designing and developing scripts for ETL processes and automation

• Dimensional and OLAP modeling

• APIs in JSON and XML

• Power BI or similar

• DevOps CI/CD: GitHub, Azure DevOps

• Agile methodologies

• Erwin data modeler or similar

• Azure Data Lake (ADLS), MS Fabric, Azure Data Factory, MS Purview.

• Python and key ML/AI libraries (e.g., scikit-learn, Pandas, NumPy, TensorFlow, PyTorch).

• Feature engineering and data preparation at scale using distributed computing frameworks like PySpark or Databricks.

• Experience in leading teams

• Excellent knowledge of data modeling and system architectures as they pertain to sourcing and consuming model features from structured and unstructured sources.

• Knowledge of data governance and compliance frameworks relevant to privacy (e.g., PIPEDA), IT Security policies and guidelines, and the practical application of Responsible AI principles (e.g., bias detection, interpretability).

Qualified candidates are encouraged to apply online on the McMaster website. We thank you for your interest; only candidates moving to the interview stage will be contacted.

Employment Equity Statement

McMaster University is located on the traditional territories of the Haudenosaunee and Mississauga Nations and within the lands protected by the “Dish With One Spoon” wampum agreement.

The diversity of our workforce is at the core of our innovation and creativity and strengthens our research and teaching excellence. In keeping with its Statement on Building an Inclusive Community with a Shared Purpose, McMaster University strives to embody the values of respect, collaboration and diversity, and has a strong commitment to employment equity.

The University seeks qualified candidates who share our commitment to equity and inclusion, who will contribute to the diversification of ideas and perspectives, and especially welcomes applications from indigenous (First Nations, Métis or Inuit) peoples, members of racialized communities, persons with disabilities, women, and persons who identify as 2SLGBTQ+.

As part of McMaster’s commitment, all applicants are invited to complete a confidential Applicant Diversity Survey through the online application submission process. The Survey questionnaire requests voluntary self-identification in relation to equity-seeking groups that have historically faced and continue to face barriers in employment. Please refer to the Applicant Diversity Survey - Statement of Collection for additional information.

Job applicants requiring accommodation to participate in the hiring process should contact:

• Human Resources Service Centre at 905-525-9140 ext. 222-HR (22247), or
• Faculty of Health Sciences HR Office at ext. 22207, or
• School of Graduate Studies at ext. 23679

to communicate accommodation needs.

Hybrid Work Language

To ensure an ongoing and vibrant University community that meets the needs of our students, staff and faculty and supports the University mission, ability to work on-site continues to be a requirement for most University positions. The University is supportive of exploring flexible work arrangements that effectively balance operational needs and employee interests.

Interview Experience

At McMaster University, we believe in a comprehensive and inclusive interview process. Our interview methods encompass a variety of approaches that allow our hiring teams to provide a flexible and accessible experience for engaging with our candidates. Throughout your recruitment process at McMaster, you may be requested to participate in a variety of formats, that may include in-person, virtual or recorded interviews. If you have any questions as you move through the hiring process, please reach out to talent@mcmaster.ca or the HR contact associated with your position of interest.