Compensation Range:

$170,000 - $200,000 CAD Compensation Disclaimer:

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, perks, or other incentives. Job Summary:

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. Key Responsibilities:

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





Key Qualifications:

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, TensorFlow, or JAX

Experience with LLM application patterns including RAG, prompt engineering, fine‑tuning, and agentic architectures

Proficiency with distributed training, GPU optimization, and experiment tracking tools

Strong foundation in statistics, probability, and mathematical optimization

Experience with advanced ML architectures such as transformers, diffusion models, graph neural networks, or reinforcement learning

Knowledge of vector databases, embeddings, and semantic search technologies

Experience in data engineering for ML, including feature stores, data pipelines, and data quality practices

Understanding of MLOps practi

📌 Lead AI Engineer (Canada)
🏢 Reonomy
📍 Canada