Senior MLOps Engineer - Artificial Intelligence (up to $290k)
About the role
This is a chance to build MLOps infrastructure for one of the most mature and critical AI operations globally. For over a decade, this team has been shipping production AI, processing vast amounts of structured and unstructured data across every asset class. Hundreds of thousands of users depend on the systems you'll keep running, making this a rare opportunity to impact AI at a level of scale and criticality few companies can offer.
You'll join a 400+ person AI department, owning the tooling and infrastructure that underpins their entire model development lifecycle. This isn't a role for someone focused solely on model experimentation; it's about designing and building the robust systems that enable ML engineers to iterate quickly and ship confidently. You'll define and enforce the SLAs around latency, throughput, and resource usage that production AI at this scale demands, working closely with both AI Platform and product-facing teams.
The work
- Design and implement continuous training pipelines for models serving hundreds of thousands of users.
- Build and optimize inference infrastructure, ensuring high throughput and low latency for critical AI products.
- Develop comprehensive monitoring and observability workflows to maintain strict SLAs on resource usage (CPU, GPU, memory, network).
- Partner with AI Platform teams to operationalize machine learning models end-to-end, from development to production.
- Collaborate directly with product-facing engineering teams to integrate and scale AI capabilities.
What you bring
- 4+ years of professional experience as a strong Python developer in production ML environments.
- Hands-on experience building or operating ML infrastructure with cloud-native tooling (e.g., Kubernetes, Argo Workflows).
- Proven ability to define and enforce SLAs for latency, throughput, and resource consumption in large-scale systems.
- Working knowledge of ML frameworks (e.g., PyTorch, ONNX, DeepSpeed) and their operational demands.
- Solid CS fundamentals and a track record of delivering production-quality code.