AI/ML Engineer

AI/ML Engineer

Location: Ontario, Canada

Work Model: 100% Remote

Onsite Requirement: Occasional visits to Toronto, ON

Experience: 8+ years of overall IT experience

Employment Type: Full-Time

Position Overview

We are looking for an experienced AI/ML Engineer with 7+ years of overall IT experience and strong hands-on expertise in designing, developing, deploying, and operationalizing AI and Machine Learning solutions.

The ideal candidate will have strong experience with Python, Machine Learning, Generative AI/LLMs, MLOps, cloud platforms, APIs, Docker/Kubernetes, and CI/CD. The candidate should be comfortable working across the full AI/ML lifecycle, from data preparation and model development through deployment, monitoring, optimization, and production support.

This is a fully remote position; however, candidates must be based in Ontario, Canada and be available to make occasional visits to the Toronto, ON office/client location.

Key Responsibilities

• Design, develop, and deploy scalable AI/ML solutions for enterprise applications.
• Develop production-quality machine learning models using Python and frameworks such as scikit-learn, PyTorch, and/or TensorFlow.
• Build end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Develop and implement Generative AI, LLM, RAG, and AI-agent solutions where applicable.
• Work with LLMs, embeddings, vector databases, prompt engineering, and model orchestration frameworks.
• Build and expose AI/ML capabilities through REST APIs and microservices.
• Implement MLOps practices including model versioning, experiment tracking, CI/CD, automated testing, deployment, monitoring, and model governance.
• Deploy and manage AI/ML workloads across Azure and/or AWS cloud environments.
• Work with services such as Azure Machine Learning, Azure OpenAI, Azure AI services, AWS SageMaker, AWS Bedrock, or equivalent platforms.
• Containerize applications and ML workloads using Docker and deploy them using Kubernetes/AKS/EKS where required.
• Build automated CI/CD pipelines using tools such as Azure DevOps, GitHub Actions, Jenkins, or similar technologies.
• Collaborate with Data Engineers to develop reliable data pipelines supporting model training and inference.
• Work with structured and unstructured data using SQL, NoSQL, data lakes, and cloud data platforms.
• Implement model performance monitoring, drift detection, logging, alerting, and automated retraining strategies.
• Apply security, privacy, responsible AI, and governance principles to enterprise AI solutions.
• Troubleshoot production AI/ML applications and resolve performance, scalability, and reliability issues.
• Collaborate with software engineers, data scientists, data engineers, DevOps teams, architects, and business stakeholders.
• Participate in technical design discussions and provide recommendations on AI/ML architecture and technology selection.
• Document technical designs, implementation approaches, deployment procedures, and operational processes.

Required Skills & Qualifications

• 7+ years of overall IT experience, with significant hands-on experience in AI/ML engineering, software engineering, data science, or related areas.
• Strong proficiency in Python.
• Strong understanding of Machine Learning algorithms, model development, evaluation, feature engineering, and optimization.
• Hands-on experience with one or more ML frameworks:
• PyTorch
• TensorFlow
• scikit-learn
• XGBoost
• Strong experience building and deploying production-grade ML solutions.
• Hands-on experience with MLOps and ML lifecycle management.
• Experience with MLflow, Kubeflow, Airflow, or equivalent MLOps/orchestration platforms.
• Strong experience with Generative AI / LLMs / RAG / Prompt Engineering.
• Experience with vector databases such as Pinecone, FAISS, Milvus, pgvector, or equivalent is an asset.
• Experience developing REST APIs and microservices using technologies such as FastAPI, Flask, or similar.
• Strong understanding of Docker and Kubernetes.
• Experience with AWS and/or Azure cloud platforms.
• Experience with cloud AI/ML services such as:
• Azure Machine Learning
• Azure OpenAI / Azure AI
• AWS SageMaker
• AWS Bedrock
• Strong understanding of CI/CD and DevOps practices.
• Experience with Git, GitHub/GitLab/Bitbucket, Jenkins, Azure DevOps, or GitHub Actions.
• Strong SQL skills and experience working with relational and/or NoSQL databases.
• Experience working with large-scale data processing platforms such as Spark/Databricks is an asset.
• Strong troubleshooting, analytical, and problem-solving skills.
• Excellent communication and collaboration skills.

Preferred / Nice-to-Have

• Experience with Agentic AI / AI Agents and frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
• Experience with RAG architecture, embeddings, vector search, and semantic search.
• Experience with Azure AI Foundry, AWS Bedrock, or Google Vertex AI.
• Experience with Kubernetes-based ML platforms and GPU workloads.
• Experience implementing model monitoring, drift detection, explainability, and responsible AI.
• Experience with Terraform or other Infrastructure as Code technologies.
• Experience working in Agile/Scrum environments.
• Cloud or AI/ML certifications such as Azure AI Engineer, AWS Machine Learning, AWS Solutions Architect, Google Professional ML Engineer, or Databricks certifications.

Candidate Location Requirement

IMPORTANT:

• Candidates must currently reside in Ontario, Canada.
• Candidates from outside Ontario will not be considered.
• This is a 100% remote position within Ontario.
• The selected candidate must be available for occasional onsite visits to Toronto, Ontario.
• Candidates must have valid authorization to work in Canada.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Mathematics, or a related field, or equivalent professional experience.

Ideal Candidate

The ideal candidate is a hands-on AI/ML Engineer who combines strong software engineering fundamentals with practical experience delivering ML, GenAI, and MLOps solutions into production. They should be comfortable working across AI development, cloud infrastructure, deployment automation, monitoring, and enterprise integration rather than focusing exclusively on model development.