Lead AI Engineer

Compensation Range:

$170,000 - $200,000 CAD

The salary range listed reflects the base pay for this role at Altus Group and is provided where required by local regulations. Actual offers may differ based on experience, market conditions, and other relevant factors. The range does not include additional compensation such as bonuses, equity, benefits, or other incentives.

This role will lead the design, development, and production deployment of advanced AI and machine learning capabilities across a commercial real estate SaaS platform. The position combines hands‑on technical leadership with applied research, building both LLM-powered features and custom domain‑specific models. The role partners closely with engineering, product, data, and architecture teams to translate complex business problems into scalable AI solutions, while establishing standards, infrastructure, and best practices for AI development across the organization.

• Architect and implement AI-powered features across the SaaS platform, including agentic workflows, intelligent data extraction, and analysis capabilities
• Lead research and development of custom AI/ML models tailored to the commercial real estate domain
• Evaluate fine-tuning foundation models versus building domain-specific models from scratch
• Establish technical standards, patterns, and best practices for AI/ML development across feature teams
• Lead hands‑on development of complex AI systems, including LLM integrations, RAG architectures, and multi‑agent orchestration
• Design and implement model training pipelines, experiment tracking, and model versioning infrastructure
• Make build‑versus‑buy decisions for AI tooling and frameworks, balancing innovation with pragmatism
• Design scalable infrastructure for AI workloads, including model serving, inference optimization, and GPU resource management
• Partner with engineering and architecture leaders to identify AI opportunities and guide implementation
• Collaborate with Platform, Data, and Analytics teams to ensure access to high-quality, unified data
• Work with product managers to translate business requirements into technical AI solutions
• Mentor engineers on AI/ML techniques, prompt engineering, and agentic frameworks
• Drive the technical roadmap for AI capabilities across applied LLM work and custom model development
• Lead research initiatives advancing CRE‑specific AI applications
• Champion AI adoption through internal knowledge‑sharing initiatives such as the AI Guild
• Evaluate emerging AI technologies and research, leading proofs‑of‑concept where appropriate
• Establish experimentation frameworks for rapid iteration and A/B testing of AI features
• Contribute to the AI/ML community through publications, blogs, or open‑source work
• Define governance models, quality gates, testing strategies, and safety measures for AI systems
• Create documentation and runbooks to support reliable operation of AI-powered features
• Balance rapid innovation with responsible AI practices and risk management

• 8+ years of product engineering experience, with at least 3 years focused on production AI/ML systems
• Proven experience training, evaluating, and deploying custom ML models in production environments
• Hands‑on experience with both LLM‑powered applications and traditional ML model development
• Deep understanding of model architectures, training methodologies, and optimization techniques
• Strong software engineering fundamentals, including system design, APIs, and cloud architectures
• Experience leading technical initiatives across teams or operating at staff or tech lead level
• Active hands‑on coder with ongoing experience writing production code and training models
• Publication record (academic papers, patents, or significant open‑source contributions) is a strong plus
• Expert‑level knowledge of ML fundamentals, including neural networks, transformers, and optimization algorithms
• Deep experience with deep learning frameworks such as PyTorch, Tensor Flow, or JAX
• Experience with LLM application patterns including RAG, prompt engineering, fine‑tuning, and agentic architectures
• Proficiency with distributed training, GPU…