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Senior MLOps Engineer (AI deployment lifecycle, CI/CD, observability)
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Senior Machine Learning MLOps Engineer to help deploy and operate AI services in a business lending environment. The role collaborates with a platform/model factory team and data science teams to take AI services through CI/CD and containerized deployments, covering environment management (DEV/UAT/PROD) and production reliability for GenAI and agentic workloads.
Responsibilities include owning the end-to-end deployment lifecycle for AI services, acting as a technical bridge between data science and platform engineering, and designing and maintaining infrastructure for LLM-based applications. This includes container orchestration, API serving layers, and integration with cloud AI services, supported by strong observability practices such as tracing, structured logging, and monitoring for latency, token usage, and cost.
The team leverages advanced analytics to deliver data-driven tools for stakeholders in the business lending domain, supporting use cases such as retention strategy recommendations, portfolio decision support through simulation and forecasting, and acceptance strategy for new applications using AI-driven optimization.
Eisen
- Containerization experience
- Graph-based AI workflow framework experience
- Relational database experience (e.g., PostgreSQL)
Wensen
- Experience with setting up a 'gold standard'/blueprint/way of working for ML models is a big plus
- Experience with implementing feature stores is an advantage
- Preferably experience within the banking sector
- Knowledge of Kubernetes is an advantage
- Experience with LangChain is an advantage