MLOps Engineer

About the role

We are looking for a mid‑to‑senior MLOps Engineer to help build the intelligence layer of our AI platform. This role goes beyond traditional pipeline maintenance, focusing on creating self‑service infrastructure for model deployment, training, notebooks, functions, UI‑driven agent creation, and RAG pipelines.

Key responsibilities

• Design and build the infrastructure powering the Intelligence layer.
• Enable reliable, automated workflows for model training, deployment, lifecycle management, and inference.
• Build scalable foundations for users to create, configure, and operate AI agents and RAG pipelines through the platform UI.
• Develop platform capabilities behind managed notebooks, functions, experiments, training jobs, model registries, and serving endpoints.
• Improve model serving, observability, versioning, evaluation, promotion, and rollback capabilities.
• Optimize GPU inference and training deployments for performance, reliability, and cost efficiency.
• Automate workflows to provide safe, self‑service capabilities for platform users.

Required profile

• Strong experience in MLOps, AI platform engineering, machine learning infrastructure, or distributed systems.
• Hands‑on Kubernetes experience, including deploying and operating stateful, training, notebook, serverless, or GPU‑intensive workloads.
• Experience building ML platforms that support training, experimentation, notebooks, feature/data workflows, model registries, and production serving.
• Familiarity with model serving frameworks such as KServe, vLLM, Triton, Ray Serve, or similar.
• Practical experience optimizing training and inference workloads for latency, throughput, availability, and cost.

Required skills

• Kubernetes
• Knative
• KServe
• vLLM
• MLflow
• LangFuse
• GPU infrastructure
• Model‑serving workloads
• RAG architectures
• Vector retrieval
• Agent workflows
• LLM applications
• Rust (strong advantage)