Senior Machine Learning (MLOps) Engineer
Senior MLOps engineer on a 12-month renewable contract (via Nöord Technologies for a major Montreal bank) building AI/GenAI initiatives for Capital Markets: designing, training and deploying ML models on AWS (SageMaker, Bedrock, Lambda), senior-level Python and REST API development, and building MLOps/LLMOps infrastructure in a hybrid Montreal role.
- Compensation
- Not disclosed
- Location
- Downtown Montreal East, Canada
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Contract
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Hands-on AWS experience (SageMaker a strong asset)
- API development (REST / gRPC) with proven ability to deploy ML models to production
- Solid foundations in Generative AI architecture (RAG / Agents)
- Excellent command of classical Machine Learning
- Senior Python development: ETL pipelines, database interactions, S3, orchestration, versioning, CI/CD on the cloud
- Strong understanding of ML and GenAI models and their real-world applications
- Experience working alongside Ops or MLOps profiles
- Tools: ML Model Lifecycle, Model Registry, CloudWatch, Bedrock, SQL, Lambda, Athena, CodeBuild, SageMaker, S3, API Gateway, MLflow, Step Functions / Airflow, EC2
- Excellent written and oral communication; explain technical issues to non-technical audiences
- Rigorous incident and knowledge documentation habits
- Fluent English mandatory; French a valuable asset
- Legally authorized to work in Canada (valid work permit, PR, or citizenship)
Nice to have
- Prior experience in Capital Markets or CIB environment
- Familiarity with market finance-specific challenges
Full description
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
Requirements
- AWS: Hands-on experience required; SageMaker experience is a strong asset.
- API Development (REST / gRPC): Proven ability to deploy Machine Learning models to production.
- : Solid foundations required; excellent command of classical Machine Learning is essential.