Senior Machine Learning Developer
BMO Financial Group1 hour ago
Toronto, CanadaSenior
Responsibilities
- Lead machine learning and AI initiatives from requirements definition through design, development, deployment, and production support.
- Translate business objectives into technical solutions, architecture designs, implementation roadmaps, and delivery plans.
- Coordinate execution across engineering, data, infrastructure, security, platform, architecture, and business teams.
- Design scalable, resilient, secure, and maintainable machine learning and cloud-native solutions using AWS technologies.
- Develop production-ready Python applications, APIs, machine learning services, and deployed models.
- Drive MLOps practices including model lifecycle management, monitoring, automation, observability, model governance, and deployment pipelines.
- Lead solution design, architectural decisions, troubleshooting, root cause analysis, project planning, estimation, and risk mitigation.
- Ensure solutions align with enterprise architecture, regulatory, security, compliance, performance, and governance standards.
- Evaluate emerging technologies and recommend improvements to platform capabilities, scalability, and business outcomes.
Requirements
- 7+ years of experience in software engineering, machine learning engineering, artificial intelligence, or a related technology discipline.
- Advanced proficiency in Python application development and experience building enterprise-grade applications, APIs, and machine learning services.
- Hands-on experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, FastAPI, or equivalent technologies.
- Strong experience designing and implementing cloud-native solutions using AWS, including SageMaker, Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, ECS, and EKS.
- Experience developing, deploying, monitoring, and optimizing machine learning models in production environments.
- Knowledge of machine learning techniques, feature engineering, model evaluation, MLOps, model governance, monitoring, and operational practices.
- Experience with APIs, microservices, distributed systems, software design patterns, testing methodologies, Git, and software development lifecycle practices.
- Demonstrated ability to lead complex enterprise technology initiatives and translate business requirements into technical solutions and implementation plans.
- Strong cross-functional collaboration, analytical, problem-solving, communication, and stakeholder management skills.
- Preferred qualifications include enterprise AI and machine learning platform delivery, generative AI, LLMs, RAG, AI-powered applications, containerization, Kubernetes, platform engineering, AWS certifications, and regulated-industry experience.
- A master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, or a related field is preferred.
Benefits
- Hybrid work arrangement in Toronto, Ontario.
- Health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans.
- Training, coaching, manager support, and network-building opportunities.
- Inclusive, equitable, and accessible workplace with accommodations available upon request.
Tech Stack
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