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Principal engineer leading backend and ML infrastructure at a Series C conversational AI company, designing real-time data pipelines, scaling GPU fleets, and optimizing inference costs for enterprise speech/NLP systems.
Build and deploy cutting-edge text-to-speech models, voice cloning, and audio generation systems using large-scale ML and transformer architectures.
Build and maintain automated testing and CI/CD infrastructure for a complex AI/ML software stack, ensuring reliability and performance across simulators, emulators, and hardware.
Lead a team to design, build, and deploy ML/AI systems for ecommerce search, recommendations, and personalization, shipping models that directly move business metrics.
Optimize inference for local LLMs and internal models using vLLM/Triton, manage GPU resources, and monitor high-load AI infrastructure for a logistics-focused AI company.
Build and optimize large-scale generative language models (LLMs) for GigaChat, focusing on Russian-language performance, distributed training, and efficiency improvements.
Builds and maintains the core backend infrastructure for AI-powered hospital automation, including EMR/OCS integrations, API servers, and model-serving pipelines in a secure on-premise environment.
Design and deploy scalable, real-time AI systems including LLM inference pipelines, RAG, and vector databases using Python, TensorFlow/PyTorch, and Kubernetes.
Design and scale backend services, APIs, and data pipelines for AI-powered media workflows, integrating generative models and optimizing performance.
Senior DevOps Engineer designs and maintains cloud infrastructure using Terraform and Azure DevOps, automates MLOps workflows, and ensures high availability of AI/ML platforms across AWS and Azure.
Overview Stats Perform is the market leader in sports tech. We provide the most trusted sports data to some of the world's biggest organizations, across sports, media, and broadcasting. Through the latest AI…
Build and scale SentinelOne’s AI Gateway (Kong AI Gateway) to route, secure, and monitor AI coding assistant traffic, while operating self-hosted LLM inference stacks and LLMOps tooling across Kubernetes.
Build and scale SentinelOne’s AI Gateway (Kong AI Gateway) to route, rate-limit, and monitor AI coding assistant traffic, while operating self-hosted LLM inference stacks and LLMOps tooling across Kubernetes and AWS.
Build and optimize AI-driven simulation models for engineering and manufacturing, scaling deep learning and distributed training across cloud and on-premise systems.
Build and own the shared AI platform that trains and serves Adobe’s generative AI models at global scale, focusing on GPU fleet utilization, low-latency inference, and end-to-end model deployment pipelines.
Build and optimize scalable generative AI inference pipelines and APIs for Adobe Firefly, integrating models into Photoshop, Illustrator, and other products while focusing on latency and performance.
Design and scale Kubernetes-native environments for distributed robotics AI workloads, including simulation, synthetic data generation, and inference using NVIDIA frameworks like OSMO and Isaac Sim.
Design and scale an enterprise AI platform using Red Hat OpenShift and OpenShift AI, focusing on container-native MLOps, LLM serving, and automated benchmarking for production deployments.
Build reinforcement-learning environments to train frontier models, blending research and engineering with Python, PyTorch/JAX, and transformer internals.
Build reinforcement-learning environments and reward functions to train frontier AI models, blending research with engineering in a startup setting.
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