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The role
This team has been building and shipping production AI for over a decade, processing critical domain data at a scale that covers tens of millions of unique entities across every asset class. They are a group of 400+ practitioners, deeply invested in search, discovery, and workflow products built on transformers, LLMs, and dense vector databases.
You'll join a growing AI engineering group as a Senior MLOps Engineer, owning the tooling and infrastructure that keeps their model development lifecycle reliable, fast, and observable. This isn't just about experimentation; it's about designing and building the production systems for continuous training pipelines, inference infrastructure, and monitoring workflows. You'll define and enforce the SLAs around latency, throughput, and resource usage that production AI at this scale demands, directly impacting hundreds of thousands of users.
The work
- Design and implement robust MLOps systems for continuous training, inference, and model monitoring, ensuring high reliability and performance.
- Define and enforce strict SLAs for latency, throughput, and resource consumption (CPU, GPU, memory, network) across production AI systems.
- Build and maintain scalable infrastructure that enables ML engineers to iterate rapidly and deploy models with confidence.
- Collaborate with AI Platform teams to operationalize machine learning models end-to-end, from development to production.
- Drive the adoption of best practices in MLOps, impacting systems relied upon by hundreds of thousands of users daily.
What You Bring
- 4+ years of professional experience as a strong Python developer, with hands-on experience in production ML environments, not just research or experimentation.
- Proven experience operating or building ML infrastructure using cloud-native tooling (e.g., Kubernetes, Argo Workflows), including defining and enforcing SLAs.
- Working knowledge of ML frameworks (e.g., PyTorch, ONNX, DeepSpeed) and a deep understanding of the operational demands of training and serving models at scale.
- Experience with at least one major cloud provider (AWS, GCP, Azure) and the ability to make sound infrastructure decisions across compute, storage, and networking.
- Strong CS fundamentals (data structures, algorithms, system design) and a track record of delivering production-quality code in collaboration with ML and software engineering teams.
Why apply through Dex
This is a rare Senior MLOps role at a company with deep, established AI capabilities and massive scale. Applying through Dex means you skip the cold application process and get a full brief on the company and role before you even interview. We match you to this role and others like it, ensuring you only consider opportunities that genuinely fit your expertise and career goals.
If you're interested, sign up to Dex to apply - https://jobs.meetdex.ai/jobs/f6c6d7fe-da06-4733-809f-fb1e24889611
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