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Build and scale ML pipelines to normalize telematics data, detect anomalies, and forecast metrics for logistics and insurance workflows using time-series and geospatial models.
At Spaceium, we are building spacecraft systems designed to push the boundaries of what's possible in orbit. As an AI Engineer, you will develop intelligent systems that enable autonomous decision-making, anomaly…
Build and maintain a production Android app in Kotlin that integrates AI-powered features like chat, vision, and voice via backend APIs, optimizing performance and reliability for real-world use.
Build and maintain scalable data pipelines that securely connect enterprise data with GenAI platforms like Azure OpenAI and AWS SageMaker, ensuring compliance and high-quality AI workloads.
Build and deploy AI-powered applications and models, including LLMs and agentic systems, using Python and full-stack development skills.
Build and deploy AI-powered expert systems for legal, tax, and compliance using RAG, custom agents, and MLOps/LLMOps pipelines in Python and cloud environments.
Design and deploy deep learning models (LLMs, CNNs, GANs) for image, text, or signal data, optimizing architectures and addressing bias and overfitting in production.
Build and deploy ML models using Python, TensorFlow/PyTorch, and MLOps tools; integrate AI features into products and optimize performance.
Design and deliver high-performance data and AI ecosystems using tools like Snowflake and TensorFlow to enable analytics and automation.
Build and deploy production-grade ML models, LLM integrations, and automation pipelines for enterprise clients using Python, TensorFlow/PyTorch, and cloud platforms.
Build and deploy production-grade AI systems using RAG, agentic frameworks (LangGraph, AutoGen), and vector search (Azure AI Search, pgvector) with Python and cloud tools.
Build and optimize ML models, LLMs, and RAG systems using Python, TensorFlow, and PyTorch, and deploy them on cloud platforms like AWS or Azure.
Build and deploy LLM-based applications using frameworks like PyTorch and Hugging Face, focusing on NLP and large language models such as LLaMA.
Senior AI Engineer designs and deploys enterprise AI systems using TensorFlow, PyTorch, and LLM APIs, building scalable ML pipelines and computer vision/NLP solutions for fintech and healthcare clients.
Build and deploy ML models for translation-related tasks using Python, TensorFlow, and cloud tools in an office-based role.
Build C++/Python frameworks and APIs to run Vision and Generative AI models efficiently on custom AI accelerators, optimizing performance and integrating with compiler/runtime teams.
Build full-stack web and mobile apps in Ruby on Rails, React, and Flutter, and integrate AI/ML models using OpenAI API, TensorFlow, and PyTorch.
Research and develop deep-learning models optimized for AI semiconductors (NPUs), including quantization, pruning, and tooling for training and deployment.
Develops and optimizes compilers and quantization/pruning techniques to accelerate AI semiconductor inference, converting models across frameworks and improving performance for NPU deployment.
Test AI NPU chips by running Windows/Linux demos, validate performance and reliability, and document issues for hardware-software integration.
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