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ML engineer builds and deploys local LLM pipelines, RAG systems, and NLP models entirely offline for a state-run transport data platform.
Leads the build-out of an internal AI and data platform to power non-engineering functions like Finance, HR, and Sales, using LLMs, agent frameworks, and modern data engineering.
Optimize and port AI inference/training kernels (SGLang, Miles) across NVIDIA/AMD GPUs, TPUs, CPUs, and emerging accelerators to maximize performance on heterogeneous hardware.
Builds and evolves RackAI, a cloud-native AI platform, using Go and Kubernetes to design scalable backend services, operators, and AI-driven tooling while shaping an AI-first engineering culture.
Principal AI Engineer designs and owns the shared AI architecture for multi-agent marketing systems, retrieval pipelines, and evaluation frameworks that power SMB-focused products at scale.
Build and optimize GPU kernels and inference frameworks (e.g., vLLM) to accelerate large language model serving, integrating research into production-grade, open-source software.
Build and optimize on-device AI inference software for NVIDIA GPUs, focusing on low-latency, memory efficiency, and deployment on RTX/DGX systems.
Lead the design and delivery of enterprise-scale AI systems, including agentic workflows, LLM applications, and MLOps tooling, using Python/Go and cloud-native stacks.
Build and maintain scalable CI/CD infrastructure for NVIDIA’s TensorRT Edge-LLM, automating builds, tests, and deployments across embedded and cloud platforms using tools like GitLab, Kubernetes, and Docker.
Design and deploy cutting-edge AI/ML applications, including LLMs and computer vision, in a defense-focused R&D environment requiring Secret clearance.
Build production-grade AI systems (RAG, agents, APIs) for logistics operations, focusing on retrieval, evaluation, and cost-efficient deployment while owning end-to-end delivery.
Job Description We are seeking experienced AI Engineers / Agentic AI Developers to design, develop, and deploy enterprise-grade AI solutions with a focus on Agentic AI , Large Language Models (LLMs) , and…
Build and fine-tune AI models for security alert triage and risk scoring using enterprise telemetry, then deploy a multi-model routing layer that keeps costs predictable while improving accuracy over time.
Build and deploy LLM-powered tools, RAG systems, and agentic workflows for autonomous aircraft systems, focusing on retrieval quality, tool integrations, and production-grade AI infrastructure on Kubernetes.
Build and secure high-performance AI inference backends using Python/Rust/C++ and cloud-native tools, integrating cryptographic attestation and mTLS to protect model weights and data.
Build secure AI infrastructure by integrating microservices, cryptographic attestation, and confidential-computing runtimes to run AI models with hardware-enforced isolation.
Senior AI Engineer at DV Trading builds and deploys custom AI models for proprietary trading, fine-tuning open-weight LLMs and operating on-prem inference infrastructure to reduce costs and latency.
Build and maintain AI inference infrastructure using Python and Kubernetes to deploy and optimize LLM services for a government customer.
Build and improve an AI shopping assistant that surfaces relevant listings, compares options, and suggests fair prices for millions of users using modern ML and LLM techniques.
Principal Machine Learning Engineer at Doctolib in Paris designs and standardizes AI/ML systems across healthcare applications, focusing on LLMs, agentic solutions, and regulatory compliance.
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