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Senior Data Engineer builds and optimizes PySpark/EMR pipelines, a Hudi-based lakehouse, and data APIs on AWS to power real-estate analytics and ML models for lead generation and property valuation.
Designs and builds scalable Azure-based data pipelines and Applied AI solutions, turning raw data into business value for sectors like finance, energy, transport, and healthcare.
Lead a team to design and build scalable Azure data pipelines and productionize ML models, using Databricks, Spark, Kafka and Python/Java.
Build and ship production-grade AI-powered applications end-to-end, blending full-stack engineering with applied AI (LLMs, RAG, agentic workflows) using Python, React, FastAPI, and cloud infrastructure.
Build and scale AI-powered backend services in Node.js/TypeScript on AWS to power On Air’s streaming platform, integrating ML models and APIs for content optimization and recommendations.
Build and optimize cloud-based software for a cleantech scale-up, integrating AI/ML models and geoscience data to map underground mineral deposits using muon tomography.
Build and deploy LLM-based AI systems for an AI-driven recruitment service, including recommendation engines and generative AI pipelines.
Build and train AI models (LLMs, CNNs, generative models) using Python and frameworks like PyTorch/TensorFlow to improve datasets and deploy solutions for real business cases.
Lead AI strategy and build large language models for a healthcare platform, collaborating with regional teams to deploy and scale ML systems end-to-end.
Mô tả công việc: Key Responsibilities Data Engineering Pipelines Build and maintain data pipelines for Amazon SP-API, Advertising API, and other e-commerce platforms. Collect and process product reviews, sales,…
Build and maintain data pipelines, clean datasets, and create dashboards to extract insights from logistics operations using Python, SQL, and Airflow.
Design, fine-tune, and deploy LLMs to automate business processes in a manufacturing environment, collaborating with cross-functional teams to scale AI solutions into production.
Build and ship production-ready AI-powered applications using Python, React, LLMs, and cloud infrastructure, turning prototypes into scalable tools that teams rely on daily.
Designs scalable Azure-based cloud architectures, data platforms, and AI/ML solutions for a motorsport engineering team, using Python, Terraform, and MLOps.
Design and build LLM-powered multi-agent systems for digital banking using Google ADK and LangGraph, integrating RAG pipelines, tool calling, and robust orchestration for production deployment.
Builds and deploys ML models in Python using TensorFlow/PyTorch and Scikit-Learn, focusing on AI system engineering for a specialized AI company.
Build and deploy ML models for anomaly detection, forecasting, and classification in aerospace systems using Python/MATLAB and PyTorch/Keras.
Build and deploy ML models, GenAI use cases, and predictive analytics for a major bank using Python, SQL, and MLOps tools.
Design and solve complex, real-world data science problems in Python and SQL to train and improve generative AI models for diverse industries.
Build and deploy AI/ML models and NLP solutions to automate business processes and support generative AI projects for clients.
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