ML/Python Engineer

Job Description — ML/Python Engineer

Location: Toronto, ON (Hybrid)

Contract Length: February 2, 2026 – January 2, 2027

Type: Contract | Hybrid (2–3 days onsite)

Max INC: $85/hr

Max T4: $79/hr

About the Role

We are seeking a highly skilled ML/Python Engineer with strong expertise in Machine Learning, Deep Learning, Reinforcement Learning, and modern agentic AI development. The ideal candidate has a deep understanding of Python-based data pipelines, model development, and the ability to build scalable, modular, and production‑ready ML systems. This role involves close collaboration with cross‑functional teams to design, develop, and deploy intelligent solutions that support key business initiatives.

Key Responsibilities

Machine Learning & AI Development

• Design, develop, and optimize ML, DL, and RL models using Python and leading AI/ML frameworks.
• Implement agentic AI workflows and autonomous model pipelines ensuring modularity and high performance.
• Build, train, evaluate, and scale models using PyTorch, TensorFlow, Keras, Scikit-learn, H2O, and other advanced ML libraries.

Python Engineering & Data Processing

• Write clean, efficient, and well‑structured Python code for data ingestion, feature engineering, model training, and inference.
• Use NumPy, Pandas, and NetworkX to manipulate, transform, and analyze large datasets.
• Implement optimized algorithms for graph-based computations, statistical modeling, and ML pipeline automation.

API & Deployment

• Develop scalable APIs using FastAPI for ML model serving and orchestration.
• Support the integration of models into production systems with a focus on reliability, performance, and security.
• Collaborate with DevOps/MLOps teams on CI/CD pipelines, containerization, and cloud deployment.

Research & Innovation

• Evaluate new ML methodologies, deep learning architectures, reinforcement learning techniques, and agentic AI frameworks.
• Prototype, benchmark, and iterate rapidly to identify the best-fit solutions for business problems.
• Document findings, model behavior, performance metrics, and engineering decisions.

Required Skills & Qualifications

Technical Skills

• Expert-level Python programming with strong grip on:
• NumPy
• Pandas
• NetworkX
• PyTorch
• Hands-on experience with ML, Deep Learning, and Reinforcement Learning algorithms.
• Practical experience with FastAPI for building high‑performance APIs.
• Strong proficiency in:
• TensorFlow
• Keras
• Scikit-learn
• H2O
• Additional modern ML libraries

AI/Agentic Frameworks

• Experience designing or integrating agentic AI models, autonomous workflows, or LLM-driven systems.
• Ability to write high-performance, modular, and reusable components for intelligent agents.