AI/Machine Learning Engineer

Armstrong Collective•Vancouver, BC

20h•Hybrid

About The Position

Reporting to Director, Data, the AI and Machine Learning Engineer is responsible for designing, building, and operating production grade machine learning systems that deliver measurable business outcomes. This is a senior individual contributor role requiring strong technical judgment, end to end ownership, and the ability to translate complex business problems into reliable, scalable AI solutions. The role partners closely with data engineering, software development, product, and business stakeholders while remaining accountable for the quality, performance, and sustainability of deployed ML systems.

Requirements

Strong ownership mindset with accountability for delivering high quality, production ready ML systems
Ability to communicate complex technical concepts clearly to both technical and non technical audiences
Sound technical judgment when making trade offs between model performance, scalability, risk, and business impact
Curiosity and adaptability in exploring new techniques, tools, and approaches
Resilience and persistence when solving ambiguous, high impact problems
5+ years of hands on experience in machine learning engineering or a closely related role, including significant experience deploying ML systems into production environments
Strong proficiency in Python and modern ML frameworks and libraries (e.g., scikit learn, PyTorch, TensorFlow, gradient boosting frameworks)
Experience deploying and operating ML models in cloud environments (AWS, Azure, or GCP), including containerization and model serving
Solid understanding of MLOps practices, including CI/CD for ML, model versioning, monitoring, and experiment tracking
Strong foundation in statistics, experimental design, and model evaluation

Nice To Haves

Experience with generative AI, LLMs, or agent based frameworks considered an asset

Responsibilities

Design, develop, and deploy scalable machine learning models and AI systems across multiple business domains, including dynamic pricing, revenue management, forecasting, classification, recommendation systems, and natural language processing
Own the full machine learning lifecycle, from problem definition and data exploration through model training, evaluation, deployment, monitoring, and iteration in production
Build and productionize pricing and revenue models that balance revenue, margin, conversion, and regulatory constraints, ensuring models operate safely and reliably in live environments
Partner with data engineers to design and maintain robust data pipelines that support machine learning systems with high quality, reliable data inputs
Collaborate with product, pricing, and business stakeholders to translate requirements into technical solutions with clearly defined success metrics tied to business outcomes
Design and execute experiments (e.g., A/B tests, causal inference, bandits) to evaluate real world impact and inform model improvements beyond offline performance metrics
Ensure strong model governance practices, including documentation, versioning, monitoring, and compliance with enterprise and regulatory standards
Monitor deployed models for performance degradation, bias, and drift, and implement retraining or mitigation strategies as required
Contribute to the evaluation and responsible adoption of emerging AI/ML techniques, tools, and platforms, including generative AI and foundation models
Provide technical mentorship, code reviews, and knowledge sharing to support team capability and engineering excellence, without direct people management accountability