AI Infrastructure Engineer

Role overview Design and maintain the core platform that trains and serves reinforcement-learning-based AI agents at production scale. The role spans scalable training pipelines, low-latency inference, and infrastructure that runs reliably across both public cloud and on-premise or edge environments for enterprise customers.

Responsibilities - Architect and operate training and inference infrastructure for RL-driven agent models, balancing scalability and reliability. - Tune model serving for latency, throughput, and cost across AWS or GCP and on-device deployments. - Build and maintain CI/CD pipelines, experiment tracking, and model versioning systems. - Develop efficient data pipelines for training data collection, preprocessing, and reward signal computation. - Partner with research scientists to productionize new algorithms and architectures. - Ensure infrastructure meets enterprise requirements around reliability, security, and compliance (e.g., SOC 2, data residency).

Requirements - 3+ years in ML infrastructure, ML platform engineering, or a closely related systems role. - Strong proficiency in Python and a systems-level language such as Rust, C++, or Go. - Hands-on experience with ML serving frameworks like vLLM, TensorRT, Triton, or ONNX Runtime. - Experience with container orchestration (Kubernetes, Docker) and cloud infrastructure on AWS or GCP. - Solid grasp of GPU computing, distributed systems, and performance profiling. - Familiarity with experiment tracking and pipeline orchestration tools such as MLflow, Weights & Biases, or Airflow.

Nice to have - Edge or on-device inference optimization (GGUF quantization, TensorRT-LLM, CoreML, QNN). - On-premise GPU deployment experience with systems like NVIDIA DGX, Dell PowerEdge, or Lenovo ThinkStation. - Background supporting RL training loops or online learning in production. - Enterprise software experience with security and compliance frameworks. - Contributions to open-source ML infrastructure projects.