Machine Learning Infrastructure Engineer
Core
Design, build, and maintain low-latency, highly scalable Python microservices and cloud infrastructure for real-time content personalization and recommendation systems.
Role type
Machine Learning Infrastructure Engineer
Builds
Scalable backend services, model serving pipelines, and real-time recommendation workflows for a global fitness and wellness platform.
Domain
Cloud infrastructure, distributed systems, and machine learning operations in the fitness/wellness technology sector.
Deliverable
production ML models
Required skills
Python, microservices architecture, cloud infrastructure management, database design (relational and NoSQL), event-driven architecture, performance tuning, observability, CI/CD, containerization (Docker/Kubernetes), API design (REST/gRPC)
Preferred skills
Java, Kotlin, Go, C, C++, experience with GCP or Azure
Technologies
Python, AWS, GCP, Azure, Kubernetes, Docker, Terraform, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, gRPC, REST, Datadog, Grafana, MLflow
Responsibilities
Design and build Python microservices for personalized content recommendations; deploy and operate ML services in cloud environments; integrate models into scalable backend workflows; ensure high availability and low latency through caching and auto-scaling; tune performance for high-traffic workloads; collaborate with platform teams on deployment workflows.
Seniority
Mid-level, hands-on IC