Senior Machine Learning Developer

Job description

Application Deadline: 10/01/2026 Address: 33 Dundas Street West Job Family Group: Technology Location: Toronto, ON (Hybrid) Overview BMO is seeking a Senior Machine Learning Engineer to join our team in Toronto. This role is ideal for an experienced technology professional with expertise in Machine Learning, Python development, AWS cloud technologies, and solution design who is passionate about leading the delivery of innovative AI and machine learning solutions at enterprise scale. As a Senior Machine Learning Engineer, you will lead complex machine learning and AI initiatives from concept through implementation. Working closely with business stakeholders, product teams, architects, data engineers, and technology partners, you will translate business objectives into scalable technical solutions and drive projects through all stages of the delivery lifecycle. The successful candidate will combine strong technical expertise with solution design and project leadership capabilities. You will be responsible for leading the delivery of strategic initiatives, influencing technical direction, and ensuring the successful implementation of secure, scalable, and high-performing machine learning solutions that deliver measurable business value. Key Responsibilities Lead the delivery of machine learning and AI initiatives from requirements definition through design, development, deployment, and production support. Partner with business and technology stakeholders to understand objectives, define technical approaches, and develop implementation roadmaps. Drive end-to-end execution of complex projects, coordinating activities across engineering, data, infrastructure, security, and platform teams. Design scalable, resilient, and maintainable machine learning solutions aligned with enterprise architecture standards and business objectives. Develop and maintain production-ready applications and machine learning services using Python and modern software engineering practices. Lead solution design activities and contribute to architectural decisions that support long-term scalability, reliability, and operational excellence. Design and implement cloud-native applications and services leveraging AWS technologies. Build, deploy, and optimize machine learning models in production environments. Drive the adoption of MLOps practices, including CI/CD, model lifecycle management, monitoring, automation, and observability. Ensure solutions meet security, compliance, performance, and operational requirements. Identify project risks, dependencies, and technical challenges, developing mitigation strategies to support successful delivery. Collaborate with Enterprise Architecture and engineering teams to ensure alignment with broader technology strategies and standards. Evaluate emerging technologies and recommend improvements that enhance platform capabilities, scalability, and business outcomes. Lead troubleshooting and root cause analysis activities for complex production issues. Support project planning, estimation, and technical delivery activities across multiple concurrent initiatives. Apply BMO's Risk Management Framework and adhere to all applicable regulatory, security, and governance standards. Required Technical Skills Python & Software Engineering Advanced proficiency in Python application development. Strong experience building enterprise-grade applications, APIs, and machine learning services. Hands-on experience with libraries and frameworks such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, FastAPI, or equivalent technologies. Strong understanding of software design patterns, testing methodologies, code quality standards, and modern development practices. AWS Cloud & Solution Design Strong experience designing and implementing cloud-native solutions in AWS. Experience with AWS services including SageMaker, Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, ECS, EKS, and related technologies. Strong knowledge of serverless, distributed, and event-driven architectures. Proven ability to design scalable, secure, and highly available solutions supporting enterprise workloads. Machine Learning & MLOps Experience developing, deploying, monitoring, and optimizing machine learning models in production environments. Strong understanding of machine learning techniques, feature engineering, model evaluation, and deployment best practices. Experience implementing MLOps capabilities, CI/CD pipelines, automation frameworks, and model governance practices. Knowledge of model monitoring, observability, and operational excellence principles. Qualifications Required 7+ years of experience in Software Engineering, Machine Learning Engineering, Artificial Intelligence, or a related technology discipline. Proven experience leading the delivery of complex machine learning, AI, data, or cloud technology initiatives within enterprise environments. Demonstrated experience translating business requirements into technical solutions, architecture designs, and implementation plans. Strong experience working across cross-functional teams, including business, data, engineering, and infrastructure stakeholders. Advanced proficiency in Python development. Strong experience designing and implementing cloud-native solutions using AWS. Experience with APIs, microservices, distributed systems, and modern architecture patterns. Strong understanding of software engineering principles, DevOps practices, testing methodologies, and the software development lifecycle. Experience with Git, CI/CD pipelines, and Agile delivery methodologies. Excellent analytical, problem-solving, communication, and stakeholder management skills. Preferred Experience delivering enterprise-scale AI and Machine Learning platforms and solutions. Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-powered applications. Experience with AWS SageMaker and cloud-based machine learning platforms. Experience wi