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Director-level AI Architect at PwC's Data & Analytics Advisory practice in Bengaluru, designing ML pipelines, LLM serving/GPU infrastructure, and LLMOps solutions for clients using cloud platforms and frameworks like MLflow, DeepSpeed, and LangChain.
The AI Infrastructure Engineer will design and operate the platform layer for large-scale AI training and inference, focusing on GPU clusters, distributed frameworks, and developer tooling. The role requires extensive experience with high-performance computing, cloud infrastructure, and ML frameworks like PyTorch and Ray.
Machine Learning/AI Scientist PhD Intern at Netflix working on ML research including personalization algorithms, recommender systems, NLP, computer vision, and other ML areas using frameworks like PyTorch and TensorFlow.
This role involves developing and optimizing distributed training solutions for large-scale machine learning models on AWS Trainium and Inferentia hardware. You will work with PyTorch, JAX, and the Neuron compiler stack to maximize performance for models like LLMs and Vision Transformers.
Builds and optimizes ML infrastructure for training and inference of warehouse robots, focusing on GPU clusters, distributed systems, and low-latency pipelines.
The AI Lead Software Architect will design and lead the execution of mission-critical, distributed AI and machine learning systems for defense and intelligence applications. This role involves architecting high-throughput inference pipelines, multi-agent frameworks, and secure, low-latency deployments across air-gapped and multi-cloud environments.
Build and optimize AI/ML systems and research software in Python/C/C++ for climate, health, and environmental projects, leveraging AWS/HPC and compiler frameworks like MLIR/LLVM.
We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your…
Lead full-stack, multi-agentic AI application engineering at RBC, building secure React/TypeScript front-ends and backend microservices (Node.js/Python/Java) on Kubernetes with CI/CD, deployed in regulated banking environments.
Overview Microsoft AI is looking for a Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We’re looking for someone who will…
Causal Labs is seeking a Machine Learning Infrastructure Engineer to build and maintain the distributed training and inference backbone for a Large Physics foundation Model. The role requires deep expertise in large-scale ML infrastructure, GPU optimization, and distributed training frameworks to support the company's mission of developing general causal intelligence.
This role involves leading QA automation initiatives for data and analytics projects, focusing on building scalable test frameworks and implementing AI agents for autonomous testing. The position requires deep expertise in GenAI, LLM orchestration, and traditional QA automation tools like Selenium and Java.
Designs and optimizes large-scale pre-training strategies for AI language models, focusing on data engineering, distributed training, and long-context techniques to enhance model performance.
The Role Training robot foundation models is expensive and iteration speed is everything: the faster our robotics engineers can launch a job, get results, and try the next idea, the faster the whole company moves. We…
The ML Developer will build and scale machine learning models and data pipelines for Sber's behavioral science laboratory. The role involves fine-tuning LLMs, designing RAG architectures, and deploying production-ready AI solutions using a stack including Python, FastAPI, and various MLOps tools.
Build and deploy AI systems for edge and embedded environments, from R&D to production, focusing on model training, evaluation, optimisation, and MLOps to deliver real-world impact in demanding settings.
Designs and deploys advanced AI/ML systems (LLMs, agentic workflows, multimodal models) for government/commercial clients, optimizing for production, edge, and security.
Design and maintain MLOps infrastructure and pipelines to streamline ML model development, training, and deployment for AI-powered developer tools.
Research Engineer building and training large language models from scratch for coding tasks, deploying them into production to power JetBrains' AI platform.
Design and build scalable, cloud-native AI infrastructure including MLOps pipelines, multi-modal feature platforms, and production-grade model training/deployment workflows using AWS, GCP, Azure, and AliCloud.
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