Job Specifications
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, benefits, 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 practices such as model monitoring, A/B testing, shadow deployments, and versioning
Cloud experience (AWS, Azure, or GCP) supporting ML workloads at scale
Strong technical communication skills with ability to explain complex concepts to diverse audiences
Pragmatic, collaborative leadership style with comfort operating in ambiguity
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