AI Engineer

MPA Recruitment • Full-time • Greater Toronto Area, Canada, CA • 5h ago

As a Senior AI Engineer, you will deliver advanced AI solutions leveraging state-of-the-art machine learning, generative and agentic AI technologies. You will drive the adoption of modern AI frameworks, AIOps best practices and scalable cloud-native architectures. Your role will involve hands-on technical delivery, collaborating with customers to translate business challenges into trustworthy AI solutions and ensuring responsible AI practices throughout. As a technical mentor, you will foster a culture of innovation, continuous learning, and engineering excellence.

It is a fast-paced environment, so it is important for you to make sound, reasoned decisions. You will do this whilst learning about new technologies and approaches, with talented colleagues that will help you to develop and grow. You will support your colleagues and more junior developers, providing direction support as you solve challenging problems together.

MINIMUM (ESSENTIAL) REQUIREMENTS:

A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics or in a similar quantitative field.
Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection).
Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc).
Experience developing solutions at scale on cloud platforms (Azure & AWS), containerisation and orchestration tools such as Kubernetes and Docker.
Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data.
Understanding of responsible AI principles, model interpretability, and ethical considerations.
Strong interpersonal skills and team working.

DESIRABLE:

Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost).
Experience with data storage for AI, vector databases, semantic search, and knowledge graphs.
Contributions to open-source AI projects or research publications.
Familiarity with AI security, privacy, and compliance standards e.g. ISO42001.