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Added 1 hour ago
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Full-time
…they meet today's needs and transition to more sustainable…
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Lorven Technologies Inc.

Senior Machine Learning Engineer for AI platform

Toronto, ON, Canada

Job type: Contract
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Original job description

Role: 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

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.

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More about this job

Responsibilities

Design, build, and deploy production-grade machine learning, LLM, and RAG solutions, including search and generative capabilities for regulated financial institutions. Develop evaluation and MLOps pipelines, document and monitor AI features, and ensure security, privacy, auditability, regulatory compliance, and effective collaboration across product and engineering teams.

Requirements

Requires senior-level experience building and deploying production ML, LLM, or NLP systems, with expertise in LLMs, RAG, NLP, semantic search, MLOps, and model evaluation. Candidates need a relevant bachelor's degree or equivalent practical experience, strong knowledge of enterprise security and responsible AI, and the ability to communicate technical concepts to non-technical stakeholders.

Skills

Machine Learning
Large Language Models
Retrieval-Augmented Generation
Natural Language Processing
Semantic Search
Model Training And Fine-Tuning
MLOps
Model Evaluation And Benchmarking
AI Pipeline Orchestration
Model Monitoring
Observability
Data Access Controls
Audit Trails
AI Privacy And Security Compliance
Responsible AI
Technical Documentation

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Large Language Models
Retrieval-Augmented Generation
Natural Language Processing
Semantic Search
Model Training
Fine-Tuning
MLOps
Model Evaluation
Benchmarking
AI Pipeline Orchestration
Model Monitoring
Observability
Operational Runbooks
Data Access Controls
Audit Trails
Data Privacy
Responsible AI
Model Cards
Technical Documentation
Enterprise SaaS
Financial Services
Regulatory Compliance
Production Software Engineering
AI Security
Scalable AI Deployments

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