BrainCo
AI Platform Engineer, Capabilities
About this role
Brain Co. seeks a backend platform engineer to design and operate the core infrastructure powering AI applications for governments and critical institutions. You'll build scalable, highly reliable backend services and data pipelines that enable rapid AI product development while meeting strict enterprise and government SLAs.
What you'll do
- Design and build production backend services and data pipelines for AI products, managing the full lifecycle from architecture to deployment
- Develop ML infrastructure systems including experiment tracking, artifact management, and automated training/evaluation pipelines
- Engineer fault-tolerant, highly available systems with deep observability to meet enterprise and government uptime requirements
- Create modular, scalable APIs (REST, gRPC) with optimization focus on latency, throughput, and cloud compute costs
- Partner with ML research, product, and engineering teams to identify and remove platform bottlenecks
- Own on-call responsibilities and long-term maintenance of production systems
What they're looking for
- Backend service design and scaling (3+ years production experience)
- Python, Go, Rust, or C++ programming
- Distributed systems and failure mode handling
- API design and documentation
- ML infrastructure tools (MLflow, Weights & Biases, Ray, Kubeflow)
- High-throughput data pipelines and asynchronous workflows
- Compliance frameworks (SOC2, FedRAMP preferred)
- System observability and profiling
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BrainCo
BrainCo builds and deploys cutting-edge AI and language model solutions for governments, healthcare systems, and critical infrastructure organizations. The company is hiring AI/ML engineers, backend platform engineers, AI platform engineers, and sales engineers to develop scalable infrastructure, production AI systems, and secure government contracts.
View all jobs at BrainCoLikely interview questions
- Walk us through a production backend service you've built and scaled. How did you approach reliability, observability, and cost optimization?
- Describe your experience designing APIs (REST, gRPC, or similar) for internal customers. How do you balance simplicity with flexibility?