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Develops AI agents and LLM-driven security solutions for Wiz’s cloud/AI platform, collaborating with engineering and security teams to enhance threat detection and automation.
Architect and maintain high-traffic LLM serving systems, optimizing throughput and latency using inference engines like SGLang, vLLM, and TensorRT alongside GPU programming tools like CUDA and PyTorch.
Senior AI Engineer designing and operationalizing ML/AI solutions—predictive models, GenAI assistants, RAG workflows, and agents—for Teradyne's IT organization, building MLOps/LLMOps pipelines and production-grade practices using Azure AI Foundry, Copilot Studio, Anthropic Claude, Vertex AI, and Snowflake.
Software engineer on Fireworks' EMEA field team in London, building product capabilities on top of a large-scale AI inference and model-serving platform — including routing, observability, and fine-tuning tooling — using Python and systems languages.
About Us: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and…
AI Forward Deployed Engineer owning customer deployments end-to-end at Fireworks—embedding in client environments to unblock production issues around capacity, model selection, latency, and integration using Python, generative AI inference/serving, and GPU infrastructure.
Research and develop AI/ML security and safety assurance methodologies for mission-critical defense systems, conducting risk analysis, threat modeling, and collaborating with engineering teams in Huntsville, AL.
The Data Scientist Principal will design and implement AI/ML solutions, including generative AI and agentic frameworks, to support government agency data initiatives. The role involves hands-on development, mentoring team members, and integrating AI capabilities into enterprise systems.
Senior Data Scientist at Grupo Modelo (AB-InBev) in Mexico City, building end-to-end analytical products and production ML models (classification, clustering, forecasting, recommendation systems) using Python, SQL, and ML frameworks.
Lead Data Scientist at Mastercard building applied ML solutions (forecasting, propensity modelling, recommendations) using Python, SQL, and ML frameworks, leading technical teams from concept to production.
Design, build, and productionize LLM-powered agentic AI systems and scalable ML pipelines for conversational interfaces using Python, cloud services (AWS), and microservices at scale.
ML Engineer II at TD's Layer 6 AI center building scalable ML frameworks and production-grade models on large financial datasets (transactions, transcripts, documents) using Python, C/C++, and PyTorch/TensorFlow.
AI Engineer building production-grade AI/ML and GenAI solutions (RAG, agents, NL interfaces) on Snowflake Cortex AI, using Python, SQL, and Microsoft Azure in a hybrid Toronto role.
Lead ML-based software development at RBC Borealis, prototyping and integrating algorithms in NLP, deep learning, and time series using Python and frameworks like PyTorch, JAX, or TensorFlow.
ML Engineer building scalable, production-grade ML systems on real-world banking data (transactions, conversations, documents) using Python, C++, PyTorch, and TensorFlow.
Build and own core AI infrastructure—model-serving pipelines, inference layers, data pipelines, and APIs/microservices—connecting GenAI, CV, and ML models to enterprise environments using TypeScript/Node.js on cloud infrastructure.
Backend AI Engineer designing and owning core AI infrastructure—model-serving pipelines, inference layers, data pipelines, and APIs/microservices—connecting GenAI, CV, and ML models to enterprise environments, primarily using TypeScript/Node.js.
Senior Java Developer building next-generation risk computation platforms for TD Securities' Counterparty Credit Risk group, using Java/Spring Boot microservices, Kafka, big data streaming, and cloud infrastructure to support billions of daily risk calculations.
Research Engineer at the Alibaba-NTU Global e-Sustainability CorpLab developing Green AI technologies—focusing on LLMs, model optimization, and efficient inference—to advance sustainable computing. Core tech: Python, PyTorch/TensorFlow, large language models, distributed training.
Own the end-to-end post-training pipeline—SFT, RL, DPO, evals, distillation, and deployment—on top of SF Tensor's custom GPU compiler and Model Foundry infrastructure, working primarily with PyTorch or JAX.
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