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Tech Talent International

Senior MLOps Engineer - Investment Banking

Montreal, QC, Canada

$140,000 to $150,000 CAD / year · Full-time

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Job details

Pay: $140,000 to $150,000 CAD / year
Job type: Full-time
Apply by: Nov 29, 2026
Posting language: English
Posted: Oct 30, 2026

Original job description

Tech Talent International (SI) supplies technical talent to a variety of clients ranging from Fortune 100/500/1000 companies to small and mid-sized organizations in Canada/US and Europe.

We currently have a role as a Senior MLOps Engineer with our large consulting client, working for a investment bank in the downtown Montreal area. T

Role: Senior MLOps Engineer - Investment Banking

Type: Permanent

Location: Hybrid - Downtown Montreal, QC -(roles starts off 5 days in office for 1st 3 months, then turns into hybrid setup 3 days onsite, 2 days from home)

Salary: Base salary ranges from $140,000 - $150,000 + 9% bonus + 3-5 weeks paid vacation + 6% RRSP matching + benefits + sick/personal day + cell phone allowance + discounts for public transit and may other perks

Being part of a dynamic team, you are convinced that technology can drive the transformation of our industry. In an agile-oriented environment, you leverage the latest market-leading technologies and solutions. Your actions will help our teams achieve our ambitious business goals by implementing a data-centric enterprise architecture dedicated to Capital Markets.

The position is located within our Global Markets IT department and Investment Bank (CIB). The Global Markets IT (GMI) department manages the global information system across the entire value chain of Global Markets businesses. The GMI teams are primarily located in Paris, London, New York, Montreal, Hong Kong, India, and Singapore.

The client is looking to strengthen its team dedicated to AI projects for Capital Markets. The engagement will focus on new initiatives related to AI and Generative AI.

Key Responsibilities

AI / Generative AI Model Design & Development

Data preparation, feature engineering, training, evaluation, versioning, deployment, and monitoring.
Select the most appropriate models and techniques based on available data and project objectives.

MLOps / LLMOps Infrastructure

Define and implement a robust MLOps architecture to support the full lifecycle of AI and Generative AI models, from design to production.
Automate development, deployment, and monitoring processes for models.
Ensure the quality, reliability, and scalability of models in production.

API Development & Python Engineering

Design and develop REST and MCP APIs.
Senior-level Python development across the full data and ML stack.

POC Industrialization

Take internal proofs of concept through to production-grade solutions.

Documentation and handover to Support

Requirements (ordered by priority)

AWS – Hands-on experience required; SageMaker experience is a strong asset.
API Development (REST / gRPC) – Proven ability to deploy Machine Learning models to production.
Generative AI Architecture (RAG / Agents) – Solid foundations required; excellent command of classical Machine Learning is essential.
Senior Python Development – Deep expertise in Python for building ETL pipelines, database interactions, S3, orchestration, versioning, and CI/CD on the cloud.
ML / Generative AI Model Knowledge – Strong understanding of ML and GenAI models and their real-world applications.
Cross-functional Collaboration – Experience working alongside Ops or MLOps profiles.
CIB Environment – Prior experience in a Capital Markets or CIB context is a strong plus; familiarity with market finance-specific challenges is valued.

Tools and Methodologies:

ML Model Lifecycle · Model Registry · CloudWatch · Bedrock · SQL · Lambda · Athena · CodeBuild · SageMaker AI · S3 · API Gateway · MLflow · Step Functions / Airflow · EC2

Communication & Competencies

Excellent written and oral communication skills
Strong ability to explain technical issues to non-technical audiences
Rigorous incident and knowledge documentation habits

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More about this job

Responsibilities

Design, develop, deploy, and monitor machine learning and Generative AI models, and build scalable MLOps/LLMOps infrastructure and APIs. Industrialize proofs of concept into production-ready solutions, document and hand them over to Support, and collaborate with cross-functional teams.

Requirements

Requires hands-on AWS experience, senior-level Python expertise, experience deploying machine learning models through APIs, and strong foundations in Generative AI architecture and classical machine learning. Experience with cloud-based data pipelines, orchestration, CI/CD, and collaboration with Ops or MLOps teams is expected; Capital Markets or CIB experience is a strong plus.

Skills

AWS
Amazon SageMaker
Python
MLOps
LLMOps
Generative AI
Classical Machine Learning
REST APIs
gRPC
Model Deployment
RAG
AI Agents
ETL Pipelines
SQL
CI/CD
Capital Markets

Benefits

Performance Bonus
Paid Vacation
RRSP Matching
Benefits
Sick And Personal Days
Cell Phone Allowance
Public Transit Discounts

Visa sponsorship

Not detected in the job text

Keywords

AWS
Amazon SageMaker
Amazon Bedrock
Python
MLOps
LLMOps
Generative AI
Machine Learning
RAG
AI Agents
REST APIs
gRPC
ETL
SQL
Amazon S3
AWS Lambda
Amazon Athena
AWS CodeBuild
Amazon CloudWatch
Amazon API Gateway
AWS Step Functions
Apache Airflow
MLflow
Model Registry
CI/CD
Feature Engineering
Model Monitoring
Capital Markets
Investment Banking
Proof Of Concept Industrialization

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