Senior Machine Learning (Mlops) Engineer

Nöord Technologies Corporation | Term to permSenior Machine Learning (MLOps) Engineer
At
Nöord Technologies, we empower top-tier financial institutions with exceptional talent in banking and capital markets. For the needs of one of our prestigious clients, a major banking actor in Montreal currently experiencing strong growth, we are actively seeking an experienced Senior MLOps Engineer.
About the Role
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. This department manages the global information system across the entire value chain of Global Markets businesses. The 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 .
Model Selection : 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 .
Process Automation : Automate development, deployment, and monitoring processes for models .
Reliability & Scalability : Ensure the quality, reliability, and scalability of models in production .
API Development & Python Engineering : Design and develop REST and MCP APIs, alongside 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 : Provide thorough documentation and ensure a smooth handover to support .
Requirements
AWS : Hands-on experience required;
SageMaker experience isa 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 Corporate and Investment Banking (CIB) context is a strong plus;
familiarity with marketfinance-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, and EC2 .
Communication & Competencies : Excellent written and oral communication skills, strong ability to explain technical issues to non-technical audiences, and rigorous incident and knowledge documentation habits .
Location : Montreal, QC (Hybrid model).
Work Calendar : Follows the Canadian market and holiday calendar .
Contract Duration : 12-month contract initially (renewable, with potential transition to a permanent position) .
Start Date : November .
Language : Fluent English is mandatory;
French is a valuable asset .
Work Authorization : Must be legally authorized to work in Canada (valid Canadian work permit, permanent residency, or citizenship).
Relocation : A comprehensive relocation package is available for eligible candidates moving to Montreal.
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