Machine Learning Engineer

Job Title: Senior Machine Learning Engineer for AI platform

Location: Toronto, ON, Canada (Hybrid)

Domain: Enterprise SaaS | Artificial Intelligence & Machine Learning | Regulated Financial Services

Role Overview

• The Senior Machine Learning Engineer is a core technical practitioner responsible for designing, building, and deploying production-grade Machine Learning, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and MLOps solutions that power the platform.
• Operating at the intersection of AI innovation and enterprise financial technology, this role works closely with product managers, solution architects, and software engineering teams to turn complex financial data and AI capabilities into scalable, secure, and reliable platform services. The Senior ML Engineer builds AI pipelines, enforces model evaluation frameworks, ensures strict security and audit compliance for regulated banking environments, and creates operational runbooks for seamless platform deployment.

Core Competencies

• Machine Learning & AI Engineering: Large Language Models (LLMs) | Retrieval-Augmented Generation (RAG) | Natural Language Processing (NLP) | Model Training & Fine-Tuning | Semantic Search
• MLOps & Platform Operations: Model Evaluation & Benchmarking | AI Pipeline Orchestration | Model Monitoring | Observability | Operational Runbooks | Scalable AI Deployments
• Enterprise Security & Responsible AI: Data Access Controls | Audit Trails | AI Privacy & Security Compliance | Responsible AI Guardrails | Model Cards & Technical Documentation
• Cross-Functional Engineering: Production Code Quality | Software Engineering Best Practices | Technical Trade-Off Communication | Cross-Functional Collaboration | AI/ML Research Integration

Technical Skills & Tools

Key Responsibilities

• Design, build, and optimize production-ready Machine Learning, LLM, and RAG solutions powering the platform.
• Implement advanced search, retrieval, and generative capabilities tailored to regulated financial institutions and enterprise banking workflows.
• Create technical documentation, model cards, evaluation notes, implementation guidance, and operational runbooks for AI/ML features.
• Build robust MLOps, evaluation, and benchmarking pipelines to continuously assess model performance, accuracy, latency, and reliability.
• Stay current with modern ML, LLM, RAG, and evaluation practices, bringing practical enhancements directly into the AI platform.
• Ensure all ML services adhere to strict software engineering standards, maintainability, and operational stability.
• Embed enterprise-grade security, data privacy, access controls, auditability, and responsible AI guardrails into all machine learning models and pipelines.
• Ensure AI capabilities satisfy regulatory compliance requirements for Tier-1 financial institutions.
• Communicate technical trade-offs, model behavior, evaluation metrics, and implementation recommendations to product managers, architects, and engineering teams.
• Partner with product leadership to evaluate new AI use cases, validate technical feasibility, and align ML capabilities with the product roadmap.

Required Skills

• Senior-level experience designing, building, and deploying ML, LLM, or NLP systems in production environments.
• Bachelor’s degree in Computer Science, Engineering, Machine Learning, Statistics, Mathematics, or a related technical field (or equivalent practical experience).
• Demonstrated expertise with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and semantic search architectures.
• Hands-on experience with modern MLOps practices, model evaluation frameworks, and model benchmarking.
• Deep understanding of enterprise security, data access controls, privacy, audit trails, and responsible AI practices.
• Strong communication skills, with the ability to explain complex ML trade-offs and model behavior to non-technical stakeholders.

Nice to Have

• Published research, open-source contributions, patents, or technical writing related to ML, NLP, search, or AI systems.
• Direct experience building AI/ML platforms for financial services or regulated enterprise software environments.