JOB DESCRIPTION:

We are looking for experienced Machine Learning Engineers who have worked under tight deadlines and on challenging tasks. The ideal candidate is a strong coder with solid machine learning engineering experience. They should also have expertise in data engineering, machine learning system design and MLOps.

EXPERIENCE AND / OR EDUCATION:

• Undergraduate degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate’s degree preferred with either progressive project work experience

Preferred : BSc+ in Computer Science, Math, Physics, or similar

• Minimum 2+ years of extensive programming experience, 1+ year experience of building machine learning production system
• Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models
• Solid experience with developing MLOps/LLMLOps CI/CD pipelines for deploying AI/ML models
• Solid experience with RAG, Agentic AI, LLM fine tuning, LLM serving, end-to-end GenAI application development, deployment, and production.
• Solid cloud experience with Azure or AWS and cloud AI/ML services such as Databricks, Kubernetes, docker and container orchestration, Azure Machine Learning, Azure Data Factory
• Strong experience with PySpark for big data processing and PyTorch for deep learning model serving
• Expert coder with Python, Java, or Scala
• Practical expertise in performance tuning, bottleneck problems analysis, and troubleshooting
• Knowledge of cloud engineering
• Self-motivated and demonstrated ability to take independent action to delivery results.
• Highly developed critical thinking, analytical and problem-solving skills
• Strong verbal and written communication skills, with the ability to work effectively across teams

WHO WE ARE:

TD is one of the world’s leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we deliver legendary customer experiences to over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to the Bank, those we serve, and the economies we support. We are guided by our vision to Be the Better Bank and our purpose to enrich the lives of our customers, communities and colleagues.
TD is deeply committed to being a leader in customer experience, that is why we believe that all colleagues, no matter where they work, are customer facing. As we build our business and deliver on our strategy, we are innovating to enhance the customer experience and build capabilities to shape the future of banking. Whether you’ve got years of banking experience or are just starting your career in financial services, we can help you realize your potential. Through regular leadership and development conversations to mentorship and training programs, we’re here to support you towards your goals. As an organization, we keep growing – and so will you.

• Predictive ML: Develop and deploy batch and real-time model inference pipelines to production, perform end-to-end integration testing.
• Gen AI: Develop and deploy scalable production Gen AI systems for Gen AI models.
• Mode Serving Framework: Develop in house model serving framework or integrate open-source model serving framework with enterprise data platform.
• ML System Design: Architect scalable machine learning and Gen AI systems that integrate with existing data platform and infrastructure, focusing on automation, operation efficiency, and reliability.
• Data Analysis & Processing: Perform data analysis, data preprocessing, and feature engineering on complex and large datasets for machine learning models.
• Model Deployment & Monitoring: Build and deploy model inference pipeline, ground truth pipeline, model monitoring pipeline to production environment. Continuously monitor production model performance and system performance
• Automation: Build CI/CD pipelines to automate model deployment, model deployment validation, model performance monitoring, and model retraining.
• Research: Stay up to date with the latest advancements in AI/ML technologies and apply them to improve existing ML systems or develop new systems and solutions.
• Technical Leadership: Provide technical expertise with a focus on efficiency, reliability, scalability, and security; includes planning, evaluating, recommending, designing, operationalizing, and supporting solutions in compliance with enterprise and industry standards.
• Collaboration: Work with AI/ML platform team, machine learning scientists, product owners and business partners to gather use case requirements and implement technical solutions for production AI/ML models.