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Наша команда отвечает за **независимую оценку и контроль качества генеративных моделей Банка.** Наша основная задача оценить флагманские генеративные модели Сбера – GigaChat, Kandinsky, GigaCode и PhysicalAI решений.…
Applied AI Engineer at an AI-native, remote-first product startup reimagining email, notes, and task tools. Day to day: own AI features end-to-end — prompt, tool, memory, and agent design, model fine-tuning and serving — turning model output into reliable product behavior using Python, PyTorch/JAX, vLLM, and vector DBs.
An Applied AI Engineer joins a remote-first, AI-native startup (client of DNA325) building AI-native replacements for email, notes, and task tools. The engineer ships AI features end-to-end — prompts, agents, memory, inference infra — using Python, PyTorch/JAX, LLM APIs, vLLM, and vector databases.
Builds and runs the ML infrastructure powering an AI-native product company's stack — covering model training, evaluation, deployment, inference, observability, and cost/performance optimization. Core technologies include Python, PyTorch/JAX, LLM serving tools (vLLM, SGLang, TensorRT-LLM), cloud and GPU infrastructure, and vector databases.
Principal Machine Learning Engineer turning research direction into production-grade ML systems for an AI-native productivity startup: building end-to-end training, evaluation, and inference pipelines, fine-tuning large models, and optimizing GPU deployment. Core stack spans PyTorch/JAX, distributed training frameworks (DeepSpeed, FSDP, Ray), and LLM inference tooling.
Weare seeking an AI Solutions Engineer (Agentic AI) to design, build and deploy enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and AI Agents. You will develop…
Senior QA Automation Engineer at Hupo, an AI-native startup building sales enablement products for banking, financial services, and insurance. Day to day you build and own automated testing of AI voice/chat agents — LLM output evals, RAG validation, STT/TTS voice pipeline checks — plus UI/E2E and API automation and CI/CD pipelines from scratch.
Forward Deployed Engineer who works directly with enterprise customers to deploy ElasticDash-managed open source stacks (Langfuse, Airflow, ClickHouse, self-hosted vLLM, etc.) into their AWS, Azure, or GCP accounts. Day to day: building backend services and automation for upgrades, migrations, monitoring, and incident response across diverse cloud environments.
Hands-on founding engineer who builds and automates low-level infrastructure: hardened nftables/Ansible firewall setups, containers, local LLM agent runtimes on bare-metal GPU clusters, PostgreSQL audit ledgers, and WireGuard mesh networking. Pure IC role with no management overhead.
Owns the infrastructure where AI workloads actually run at AZX: the inference control plane serving open-weight LLMs across GPU clouds and customer Kubernetes clusters, plus a microVM-based agent-sandboxing platform for untrusted agent code. Daily work spans Rust systems programming, Kubernetes operators/autoscaling, and FastAPI control-plane services.
AI Engineer in Abuja building and owning production LLM-powered systems end-to-end — agents, RAG, context engineering, AI evaluation frameworks, and model fine-tuning/deployment — using Python, FastAPI, PyTorch, Hugging Face, and inference frameworks like vLLM, with focus on security, cost, and reliability.
Remote Senior AI Engineer who owns the AI layer of Techsa's technology and data platform: building retrieval/RAG, agent and tool-calling systems, and natural-language interfaces over enterprise data, with evaluation to measure quality. Core stack is Python and LLM application engineering, with optional vLLM serving and fine-tuning.
Holyweb, a service (outstaffing-style) IT company, is hiring a Middle+/Senior Backend Developer for a corporate GenAI products project. The role involves building production-ready APIs and microservices in Python/FastAPI, LLM/agent workflows and enterprise RAG services, with PostgreSQL, RabbitMQ/Kafka, Redis, Docker and Kubernetes.
MLOps engineer to stand up and operate self-hosted LLM inference (vLLM/SGLang on NVIDIA GPUs) behind a LiteLLM gateway in an air-gapped corporate network: tune throughput/latency, monitor with Prometheus/Grafana, manage secure model updates, build a RAG layer over code/docs, and roll out AI coding tools to teams.
Data Scientist building AI agents on generative models (GigaChat and open-source) for a new large-scale Sber group project, and designing offline/online metrics to evaluate LLM application quality. Core stack: Python, PyTorch, Transformers, LangChain/LlamaIndex/LangGraph, plus MLOps tools like Docker, LangFuse, Ollama, vLLM.
Mount Thor is hiring a founding inference engineer to build a production inference platform on Apple Silicon. You will own the stack from GPU kernels and model execution to distributed scheduling, networking, and…
A customer-facing engineer in Munich who embeds with enterprise clients across DACH to architect, prototype, and launch production systems on Telnyx's voice, messaging, AI, and wireless APIs — including LLM gateways and AI voice assistants — partnering with an AE from technical discovery through go-live. Core stack: Python/TypeScript/Go, Kubernetes, Kafka, LLM tooling, and observability.
GALT Aerospace is hiring a full-time, onsite Software Engineer II/III in San Diego to build full-stack and distributed software for aerospace and defense systems — Python backends, Vue.js frontends, Podman containers, and Ansible automation on RHEL/Windows, deployed in secure air-gapped environments, with growing AI/LLM integration. U.S. citizenship required.
Full stack software engineer at SEMIC|Econocom's innolab innovation team in Lleida, Spain, building and deploying generative AI solutions for clients: deploying LLMs/SLMs with vLLM, prompt engineering, Docker-based on-prem/cloud deployments, and AI agents with LangChain/LangGraph, on an Angular/Docker/SQL full stack foundation.
Strategic software engineering lead at AMD who works hands-on with strategic customers to optimize AI workloads on AMD GPUs — tuning collective communication primitives (NCCL/RCCL), attention and KV-cache kernels, and LLM serving frameworks using C/C++, Python, and CUDA/HIP, from proof of concept through production.
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