AI Platform Engineer, Capabilities

About the role

Design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products. You will own the full lifecycle: from initial architecture and implementation to deployment and long-term maintenance.

Build the critical systems that accelerate our AI product development. This includes designing scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines.

Engineer highly available, fault-tolerant systems with deep observability. Your architectures must be robust enough to meet the strict uptime and latency SLAs demanded by our enterprise and government clients.

Design modular and scalable architectures, and clean APIs (REST, gRPC) with a long-term platform mindset. Continuously profile systems to ruthlessly optimize for latency, throughput, and cloud compute costs.

Act as the bridge between engineering, product, and ML research. You will partner with these teams to build shared platform capabilities that remove bottlenecks and drastically reduce the time it takes to ship new AI products to our clients.

Responsibilities

• Design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products.
• Own the full lifecycle: from initial architecture and implementation to deployment and long-term maintenance.
• Build the critical systems that accelerate our AI product development.
• Design scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines.
• Engineer highly available, fault-tolerant systems with deep observability.
• Design modular and scalable architectures, and clean APIs (REST, gRPC) with a long-term platform mindset.
• Continuously profile systems to ruthlessly optimize for latency, throughput, and cloud compute costs.
• Act as the bridge between engineering, product, and ML research.
• Partner with these teams to build shared platform capabilities that remove bottlenecks and drastically reduce the time it takes to ship new AI products to our clients.

Requirements

You bring 3+ years of experience building and scaling production backend services or platforms, ideally using languages common in the AI/Data ecosystem (e.g., Python, Go, Rust, or C++).

You don't just use frameworks; you understand what happens under the hood.

You have a deep grasp of consistency, availability, distributed failure modes, and idempotency.

You treat our internal ML and product teams as your primary customers.

You have a strong intuition for building intuitive, well-documented, and highly maintainable APIs and shared platforms.

You have a track record of owning services with real uptime expectations.

You design for observability from day one and aren't afraid of on-call responsibilities for the systems you build.

You excel at breaking down complex, open-ended problems into clear technical designs, moving from first principles to production-ready systems with both speed and rigor.

You treat the platform as your own. You care about the end-to-end lifecycle of your systems.

Bonus points

• Integrated or managed standard ML experiment tracking, artifact management, and model registries (e.g., Weights & Biases, MLflow, ClearML) or orchestrators (e.g., Ray, Flyte, Kubeflow).
• Designed and operated high-throughput data pipelines and resilient asynchronous workflows using durable execution engines (e.g., Temporal), streaming platforms (e.g., Kafka), or distributed compute frameworks (e.g., Spark, Flink).
• Navigated strict compliance frameworks (e.g., SOC2, FedRAMP) or built systems specifically for highly regulated, air-gapped, or on-premise environments.