AI Engineer - Banking Domain
At a glance
Mid-level AI Engineering role at Jay Analytix. Montreal · contract.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and put machine-learning and generative-AI systems into use for fraud, AML, credit scoring and customer analytics
- Create LLM tools for internal work, including retrieval-augmented generation pipelines and document analysis
- Set up model tracking, automated delivery, monitoring and drift checks as part of MLOps
- Apply banking model governance and privacy rules, then explain model results and business effects to varied audiences
What you bring
- At least seven years in AI or machine-learning engineering, including three or more in banking or financial services
- Strong Python skills and practical work with ML frameworks, MLOps platforms, LLM development and a cloud provider
- Knowledge of OSFI E-23 governance, PIPEDA and explainable AI for audit work
- Experience using SQL alongside distributed data platforms such as Spark, Databricks or Snowflake
Who this fits
This contract suits an experienced AI engineer who has taken banking models from development into production and can work with technical and non-technical partners. The engagement runs for twelve months and may be renewed, with a hybrid model in Toronto, Montreal, Vancouver or Calgary. Canadian work authorization is required.
From the employer
Engagement
Contract — 12 months, renewable
Domain
Banking & Financial Services
Locations
Toronto, ON | Montreal, QC | Vancouver, BC | Calgary, AB
Work Model
Hybrid
Start
Immediate
ABOUT THE ROLE
We are looking for an experienced AI Engineer to design and deliver production-grade machine learning and generative AI solutions within a major Canadian bank. You will work across fraud detection, credit risk, regulatory compliance, and customer analytics, partnering with data, engineering, and compliance teams to bring AI from prototype to production.
KEY RESPONSIBILITIES
- Build and deploy ML and GenAI solutions for banking use cases (fraud, AML, credit scoring, customer analytics).
- Design LLM-based applications including RAG pipelines and document intelligence for internal workflows.
- Implement MLOps best practices: model versioning, CI/CD, monitoring, and drift detection.
- Ensure compliance with OSFI model risk guidelines, PIPEDA/CPPA, and internal governance frameworks.
- Communicate model performance and business impact to technical and non-technical stakeholders.
MUST-HAVE
- 7+ years in AI/ML engineering, with 3+ years in banking or financial services.
- Advanced Python skills: PyTorch/TensorFlow, Scikit-learn, Pandas.
- Hands-on MLOps experience: MLflow, Kubeflow, Azure ML, or SageMaker.
- LLM/GenAI development: OpenAI, Azure OpenAI, LangChain, RAG architectures.
- Cloud proficiency: Azure (preferred), AWS, or GCP.
- Knowledge of OSFI E-23 model governance, PIPEDA, and explainable AI for audits.
- Experience with SQL and distributed data platforms (Spark, Databricks, or Snowflake).
GOOD TO HAVE
- Azure AI-102, AWS ML Specialty, or Google Professional ML Engineer certification.
- Exposure to IFRS 9, Basel III, or Open Banking frameworks.
- Experience with real-time ML inference (Kafka, Flink).
- Bilingual English/French (asset for Montreal).
- FRM or CFA designation as a complement to technical skills.
HOW TO APPLY
Submit your resume, preferred location, and available start date. Canadian work authorization required.