Software Engineer II - Machine Learning (B3617)

TD•Toronto, ON

4d•Onsite

About The Position

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

Requirements

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
2+ years of extensive programming experience
1+ year experience of building machine learning production systems
Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models
Solid experience with developing MLOps/AIOps 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

Nice To Haves

Knowledge of cloud engineering
Self-motivated and demonstrated ability to take independent action to deliver results.
Highly developed critical thinking, analytical and problem-solving skills
Strong verbal and written communication skills, with the ability to work effectively across teams

Responsibilities

Develop and deploy scalable production Gen AI systems and applications.
Develop and deploy batch and real-time model inference pipelines to production, perform end-to-end integration testing.
Develop in-house model serving framework or integrate open-source model serving framework with enterprise AI and data platform.
Architect scalable machine learning and Gen AI systems that integrate with existing AI and data platforms and infrastructure, focusing on automation, operation efficiency, and reliability.
Perform data analysis, data preprocessing, and feature engineering on complex structured and unstructured and large datasets for machine learning models and AI applications.
Build and deploy model inference pipeline, ground truth pipeline, model monitoring pipeline to production environment. Continuously monitor production model performance and system performance.
Build CI/CD pipelines to automate model deployment, deployment validation, model performance monitoring, and model retraining.
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.
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.
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 and applications.