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Build and ship AI features end-to-end: design agent workflows, fine-tune models, and turn outputs into reliable production systems using Python, PyTorch/JAX, and modern LLMs.
Обязанности: Сформировать дорожную карту ML‑инициатив для трейдинга и смежных функций (ценообразование, прогнозы спроса/предложения, оптимизация логистики, риск‑метрики); Разрабатывать end-to-end LLM-приложения : от…
Build and maintain ML infrastructure to deploy and scale AI models in production, using Python, Kubernetes, and Ray. Own platform services that enable data scientists to move models from experimentation to live systems.
Build and maintain the infrastructure that trains, deploys, and serves AI models at scale, using Python, PyTorch/JAX, and LLM serving stacks like vLLM.
Build and optimize LLM-powered AI assistants for a bank’s internal support systems using Python, RAG, and vector databases.
Build and maintain ML pipelines and AI infrastructure for banking products, using Kubernetes, Docker, Terraform, and cloud platforms to deploy and monitor ML models at scale.
Build and optimize LLM-powered AI assistants for a bank’s customer service channels, integrating models like GPT and Llama with internal APIs and RAG architectures.
Principal engineer leading backend and ML infrastructure at a Series-C conversational AI company, scaling real-time speech/NLP systems, GPU fleets, and inference pipelines for enterprise contact centers.
Build and optimize computer-vision models for Datature’s MLOps platform, integrating cutting-edge neural networks and prototyping new vision algorithms for clients in niche domains like medicine and agriculture.
Lead the AI and data platform for a live-commerce marketplace, building pipelines, personalization, and automation systems that power buyer experiences, seller tools, and marketing campaigns using Python, ClickHouse, and LLMs.
Build AI agents and automation scripts for an internal AI-powered software engineering platform, integrating LLMs and testing tools to improve developer workflows.
Senior DevOps/SRE engineer building and scaling AWS and Azure cloud infrastructure, Kubernetes/ECS clusters, CI/CD pipelines, and AI inference workloads while enforcing security, observability, and FinOps practices.
Senior DevOps Engineer to design, build, and operate scalable infrastructure for an enterprise AI platform, using Kubernetes, cloud providers, and CI/CD pipelines.
Builds and optimizes high-performance ML systems for TPU hardware using JAX, XLA, and Pallas, focusing on inference/training workloads and compiler/runtime tuning.
Builds and optimizes large-scale AI inference systems for frontier models, focusing on performance, latency, and cost across thousands of GPUs.
Principal Architect leads the design of a GPU-powered simulation platform for AI data centers, enabling real-time power orchestration and validation of energy-aware compute systems.
Builds and maintains developer-facing tools (APIs, SDKs, dashboards) on top of AI inference/training infrastructure like SGLang and Miles, collaborating with product and research teams to create intuitive interfaces.
Optimize and accelerate LLM inference and training systems like SGLang and Miles by profiling GPU performance, writing custom kernels, and enabling new models on modern hardware.
Build and ship production-grade AI systems—LLMs, agents, retrieval, and evals—from prototype to scalable deployment for high-stakes decision-making.
Senior AI Engineer Remote — US only · Full-time Are you passionate about building AI products people actually use, serving millions of users? Do you want to help lead the AI engineering effort at this country's…
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