GenAI ML Engineer Toronto, ON (Hybrid) Fulltime FTE

Job Title: GenAI ML Engineer
Location: Toronto ON Canada
Work Model: Onsite (4 Days/Week)
Job Type: Full-Time Permanent - FTE
Experience Required: 6 8 Years

Job Summary

We are seeking an innovative and highly skilled GenAI ML Engineer to join our AI Engineering team in Toronto. The ideal candidate will have strong expertise in Generative AI Machine Learning Large Language Models (LLMs) and Python with hands-on experience building fine-tuning and deploying production-grade AI solutions.

In this role you will design and develop cutting-edge generative AI applications optimize foundation models implement scalable ML pipelines and collaborate with cross-functional teams to deliver enterprise AI solutions. This is an excellent opportunity to work with the latest advancements in Generative AI including LLMs transformer models and modern MLOps practices.

Key Responsibilities Generative AI Development

• Design develop and implement generative AI solutions for text code and multimodal applications.

• Build AI-powered applications leveraging state-of-the-art foundation models.

• Develop innovative AI capabilities to solve complex business challenges.

Large Language Model (LLM) Engineering

• Fine-tune optimize and deploy Large Language Models (LLMs) such as GPT Claude Llama and similar transformer-based models.

• Customize foundation models for enterprise-specific use cases.

• Implement Retrieval-Augmented Generation (RAG) and prompt optimization techniques.

Machine Learning & Model Development

• Develop and train custom machine learning and deep learning models.

• Build scalable model training pipelines for foundation model adaptation.

• Optimize neural networks for performance latency and accuracy.

• Work with transformer architectures and modern deep learning techniques.

Python Development

• Develop robust backend AI services and APIs using Python.

• Build reusable ML components and scalable AI applications.

• Integrate AI models into enterprise applications and workflows.

Prompt Engineering

• Design test and optimize prompts for various LLM use cases.

• Improve model responses through prompt tuning and contextual optimization.

• Develop reusable prompt libraries and evaluation strategies.

Model Evaluation & Responsible AI

• Build evaluation frameworks to measure model quality accuracy safety and bias.

• Monitor AI model performance and continuously improve outputs.

• Implement responsible AI practices and governance controls.

MLOps & Production Deployment

• Deploy machine learning models into production environments.

• Build scalable inference pipelines and model serving infrastructure.

• Monitor model health drift and production performance.

• Implement CI/CD pipelines for machine learning workflows.

Data Engineering & Feature Pipelines

• Develop data preprocessing transformation and feature engineering pipelines.

• Prepare structured and unstructured datasets for model training.

• Optimize data workflows for large-scale AI applications.

Research & Innovation

• Stay up to date with the latest advancements in Generative AI LLMs and machine learning research.

• Evaluate emerging AI frameworks and technologies.

• Prototype innovative AI solutions using state-of-the-art techniques.

Collaboration

• Work closely with Product Managers Data Scientists Software Engineers and Business Stakeholders.

• Participate in architecture discussions code reviews and technical planning.

• Contribute to AI strategy and best practices across engineering teams.

Required Skills

• 6 8 years of experience in Machine Learning and AI Engineering.

• Strong expertise in Generative AI and Large Language Models (LLMs).

• Expert-level programming skills in Python.

• Strong understanding of machine learning algorithms deep learning and neural networks.

• Hands-on experience with:

  • PyTorch

  • TensorFlow

  • Hugging Face Transformers

  • LangChain

• Experience with:

  • LLM fine-tuning

  • Prompt Engineering

  • Retrieval-Augmented Generation (RAG)

  • Foundation Model adaptation

• Experience building and deploying production ML pipelines.

• Knowledge of GANs VAEs diffusion models and transformer architectures.

• Strong understanding of MLOps practices including:

  • Model versioning

  • Model monitoring

  • CI/CD for ML

  • Model deployment

• Experience working with cloud platforms:

  • AWS

  • Microsoft Azure

  • Google Cloud Platform (GCP)

• Familiarity with GPU computing and distributed model training.

• Strong analytical debugging and problem-solving skills.

Preferred Qualifications

• Experience working on enterprise-scale AI/ML platforms.

• Knowledge of vector databases and semantic search.

• Experience integrating LLMs into production applications.

• Familiarity with AI safety governance and responsible AI practices.

• Experience building scalable AI microservices and APIs.

• Understanding of containerization technologies such as Docker and Kubernetes.

• Experience working in Agile/Scrum environments.

Required Technologies

• Python

• Machine Learning

• Deep Learning

• Generative AI

• Large Language Models (LLMs)

• GPT

• Claude

• Llama

• PyTorch

• TensorFlow

• Hugging Face

• LangChain

• Transformer Models

• GANs

• VAEs

• Diffusion Models

• Prompt Engineering

• MLOps

• CI/CD

• AWS

• Azure

• GCP

• GPU Computing

Soft Skills

• Excellent communication and stakeholder management skills.

• Strong analytical and critical thinking abilities.

• Passion for AI innovation and continuous learning.

• Ability to work independently and collaboratively in cross-functional teams.

• Strong attention to detail and commitment to delivering high-quality solutions.

Employment Type : Full Time
Experience: years
Vacancy: 1