Principal AI/ML Architect – Recommendation Systems & ML Platforms Company: Vasa Digital Employment Type: Contract / Consulting

Location:
Remote – Canada, US preferred Start: ASAP About Vasa Digital Vasa Digital is a specialist enterprise architecture and AI consulting firm helping large organizations modernize technology platforms, develop scalable architectures, and deliver measurable business outcomes. We are seeking an experienced Principal AI/ML Architect to support a major enterprise client modernizing its recommendation and personalization platforms. The Opportunity Our client is evaluating a transition from existing clustering-based recommendation algorithms to modern two-tower deep learning architectures, alongside decisions around Google Cloud versus Databricks, real-time personalization, and A/B experimentation. We need a technically strong architect who understands both machine learning algorithms and the infrastructure required to deploy and operate them at enterprise scale. This is a hands-on ML architecture leadership role, not a general GenAI or traditional enterprise architecture position.

Key Responsibilities
Recommendation Systems & Personalization
• Architect up-to-date recommendation systems using two-tower models, embeddings, candidate retrieval and ranking algorithms.
• Evaluate existing clustering-based approaches against modern ML architectures.
• Guide model evaluation, performance optimization and phased modernization. ML Platform & Infrastructure Architecture
• Evaluate Google Cloud / Vertex AI versus Databricks, including architectural trade-offs, scalability and cost.
• Design ML training, deployment, feature management and model-serving infrastructure.
• Establish scalable, secure and production-ready ML platform architectures. Real-Time Inference & MLOps
• Design low-latency,



high-throughput inference architectures incorporating real-time customer signals.
• Architect streaming data pipelines, online feature retrieval, APIs and model-serving services.
• Establish MLOps patterns covering CI/CD, monitoring, model versioning, retraining and deployment. A/B Testing & Experimentation
• Design enterprise A/B testing and experimentation frameworks.
• Define model evaluation, traffic allocation, experimentation and performance measurement approaches.
• Enable controlled testing and deployment of new recommendation algorithms. Technical Leadership
• Provide architectural direction to data scientists, ML engineers and platform teams.
• Develop reference architectures, technical recommendations and implementation roadmaps.
• Communicate architectural decisions and trade-offs to senior technology stakeholders. Required Technical Skills
• Recommendation Systems: Two-tower models, collaborative filtering, embeddings, candidate retrieval, ranking and personalization.
• Machine Learning: Deep learning, TensorFlow/PyTorch, Python, feature engineering and model optimization.
• Google Cloud: Vertex AI, BigQuery, GKE and ML platform architecture.
• Databricks: MLflow, Spark, model lifecycle and serving infrastructure.
• Real-Time ML: Low-latency inference, streaming pipelines, feature stores and scalable model serving.
• MLOps: Model deployment, CI/CD, orchestration,



monitoring and production ML operations.
• Experimentation: A/B testing frameworks, statistical evaluation and model-performance measurement.
• Cloud Infrastructure: Kubernetes, Docker, distributed systems and API architectures. Experience & Qualifications
• Typically 10+ years in software engineering, machine learning, data engineering or related fields, with significant production ML architecture experience.
• Proven experience architecting production recommendation or personalization systems.
• Strong experience with Google Cloud / Vertex AI and/or Databricks.
• Experience deploying and operating ML models at scale.
• Ability to balance technical depth with architecture leadership and business decision-making.
• Strong communication skills and experience working with enterprise engineering teams. Highly Desirable
• Experience in retail, e-commerce, digital advertising, media or streaming.
• Hands-on experience with two-tower recommendation architectures.
• Experience modernizing legacy recommendation engines.
• Experience building real-time personalization and experimentation platforms.
• Experience comparing ML platform alternatives and defining migration roadmaps. What Success Looks Like You will help the client evaluate and modernize its recommendation architecture, establish the right ML platform strategy, enable real-time personalization, and guide the transition from R&D; through production deployment.

How to Apply
Please share your resume or LinkedIn profile, highlighting:
• Recommendation platforms you have architected.
• Experience with two-tower models and production ML systems.
• GCP / Vertex AI and Databricks expertise.
• Current availability and contract preferences.

📌 Principal AI (Canada)
🏢 Vasa Digital Architects
📍 Canada