Applied Scientist (AI Engineering)

TL;DR

Applied Scientist (AI Engineering): Designing, prototyping, and productionizing state-of-the-art AI systems focusing on knowledge-centric AI, graph-based learning, and advanced Retrieval-Augmented Generation (RAG) architectures. Focus on combining LLMs, knowledge graphs, vector databases, graph neural networks, and multimodal models to power intelligent search, reasoning, and personalization at scale for enterprise environments.

Location: Hybrid, 4 days per week in the downtown Toronto office, Canada

Salary: $110,000 - $150,000 CAD

Company

hirify.global helps Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact.

What you will do

• Conduct applied research to solve real-world problems using LLMs, graph-based models, and multimodal AI.
• Design and implement hybrid AI architectures combining knowledge graphs, vector search, and deep learning for reasoning-aware systems.
• Research and implement graph embeddings, Graph Attention Networks (GATs), and Graph Neural Networks (GNNs).
• Design and build advanced RAG systems, optimizing retrieval pipelines for latency, relevance, grounding, and explainability.
• Design and implement LLM agent systems, including multi-agent orchestration, tool use, planning, and memory.
• Build end-to-end AI pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring on AWS or GCP.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Data Science, or a related field.
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred).
• Hands-on experience with applied research and translating research ideas into production-grade AI systems.
• Proven experience with knowledge graphs, graph embeddings, or graph neural networks.
• Experience building advanced RAG systems using vector databases and structured knowledge sources.
• Strong understanding of LLMs, embeddings, and fine-tuning techniques, with experience deploying AI systems in enterprise environments.

Nice to have

• Experience with graph databases (e.g., Neo4j, Neptune) and vector stores.
• Experience with agent-based LLM systems and multi-agent strategies.
• Experience with MLOps tools (SageMaker, Vertex AI, MLflow, feature stores).
• Familiarity with time-series forecasting, recommendation systems, or personalization models.

Culture & Benefits

• Hybrid work environment with 4 days per week in the downtown Toronto office.
• Comprehensive benefits including 100% coverage for health, dental, and vision insurance for you and your dependents from day one.
• Opportunity to build AI systems used by Fortune 500 companies.
• Continuous learning opportunities and influence over technical direction.
• Shape applied research and AI strategy in a fast-growing, product-focused data company.