Machine Learning Architect

Jarvis is currently seeking an experienced Machine Learning Architect to lead the technical delivery of production-grade machine learning solutions for our clients. This is a hands-on engineering role with strong ownership expectations. Your primary responsibility is building scalable, secure, and maintainable ML systems — while taking ownership of significant components of solution delivery.

In this role you would be responsible for turning architectural designs into fully implemented, production-ready systems. You are comfortable operating with moderate ambiguity and can break down high-level designs into structured implementation plans. You set the standard for code quality, reliability, and engineering discipline within the team.

You will work within a Databricks-based environment (Azure preferred; AWS or GCP acceptable if Databricks expertise is strong). This role requires strong coding ability, end-to-end ML lifecycle expertise, and the ability to guide other engineers through delivery.

• Own and implement end-to-end ML solutions including feature engineering, model development, evaluation, deployment, and monitoring.
• Design and build scalable batch and/or streaming pipelines within Databricks.
• Develop, optimize, and productionize traditional ML models (e.g., regression, classification, tree-based models such as XGBoost).
• Lead implementation of Generative AI solutions including LLM-based systems, RAG pipelines, prompt engineering workflows, and agent-based applications.
• Deploy models as API endpoints, real-time inference services, or scheduled batch jobs.
• Implement robust experiment tracking, model versioning, CI/CD workflows, and lifecycle management practices.
• Establish logging, monitoring, observability, and performance tracking for ML systems in production.
• Break down architectural designs into actionable engineering tasks and ensure structured execution.
• Review and approve PRs/MRs, maintaining high standards of code quality and maintainability.
• Mentor and support junior Machine Learning Engineers through technical guidance and feedback.
• Proactively identify risks, technical gaps, and scalability concerns early in delivery.
• Ensure solutions meet security, compliance, scalability, and performance requirements.

• 5+ years of experience in Machine Learning Engineering or a closely related role.
• Proven experience delivering end-to-end ML systems in production environments.
• Strong hands-on coding ability in Python with clean, maintainable engineering practices.
• Deep understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
• Experience with Databricks (required); Azure preferred. AWS or GCP acceptable if Databricks expertise is strong.
• Strong experience with supervised learning techniques (e.g., regression, classification, tree-based models) and clear understanding of when to apply them.
• Experience building and deploying Generative AI applications, including LLM integrations, RAG pipelines, and agent-based systems.
• Understanding of CI/CD pipelines.
• Strong experience reviewing and approving code (PRs/MRs).
• Ability to take ownership of significant solution components and deliver with minimal supervision.
• Experience mentoring or guiding junior engineers in a structured way.

Jarvis is driven by a bold vision to transform the professional services landscape by setting a new standard for innovation, empowerment, and impactful solutions in data, AI, and technology. Our unwavering dedication is rooted in propelling businesses toward sustained success while making a positive impact on communities. Strategically operating from Toronto and Montreal, Europe, and North Africa