Senior MLOps Engineer

Mission
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 at the client. 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
1. 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.
2.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.
3. API Development & Python Engineering
Design and develop REST and MCP APIs.
Senior level Python development across the full data and ML stack.
4.POC Industrialization
Take internal proofs of concept through to production grade solutions.
5. Documentation and handover to Support
Requirements (ordered by priority)
1. AWS – Hands on experience required; SageMaker experience is a strong asset.
2. API Development (REST / gRPC) – Proven ability to deploy Machine Learning models to production.
3. Generative AI Architecture (RAG / Agents) – Solid foundations required; excellent command of classical Machine Learning is essential.
4. Senior Python Development – Deep expertise in Python for building ETL pipelines, database interactions, S3, orchestration, versioning, and CI/CD on the cloud.
5. ML / Generative AI Model Knowledge – Strong understanding of ML and GenAI models and their real world applications.
6. Cross functional Collaboration – Experience working alongside Ops or MLOps profiles.
7. 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