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Design, train, and deploy AI/ML models and generative AI solutions using cloud-native platforms like GCP Vertex AI, Azure ML, or AWS SageMaker.
Build predictive models and dashboards for banking and government clients using Python, SQL, and Power BI to turn raw data into actionable insights.
Build and deploy ML/AI models (regression, classification, deep learning, LLMs) for business use cases, integrate outputs into Power BI dashboards, and productionize solutions for clients.
Design and deploy AI/ML and GenAI solutions, building predictive models and scalable data pipelines to drive business decisions and customer outcomes.
Build and deploy ML models to detect anomalies in time-series sensor data for climate/industrial systems using Python, SQL, and MLOps practices.
Build and deploy ML models and deep-learning systems in Python, integrating them into production apps and APIs while collaborating with cross-functional teams.
Build and deploy production-grade AI/ML models and features, focusing on deep learning, MLOps, and end-to-end automation for measurable business impact.
Builds and deploys ML models to optimize marketing spend and forecast demand, using Python, SQL, and basic neural networks with mentorship from HQ’s data-science team.
Build and deploy ML models for predictive maintenance and energy optimization using Python, FastAPI, and Azure ML in ETAP’s electrical digital twin platform.
Build and maintain AWS-based data pipelines and warehouses using Python, SQL, and services like Glue, Redshift, and S3 to enable analytics and ML feature stores.
Build and maintain ML models and ETL pipelines in Python to power business decisions and dashboards.
Designs and builds AWS-based data pipelines and feature stores using Python, SQL, and tools like Glue and Sagemaker to support analytics and ML workloads.
Build and maintain scalable data pipelines and infrastructure to power AI/ML features in Tebra’s healthcare EHR platform, ensuring reliable, high-quality datasets for model training and production inference.
Build and own end-to-end data pipelines for OKX’s global compliance systems (TMS, KYC/EDD, CMS, regulatory reporting) using Python, SQL, and orchestration tools.
Build and own a large-scale Web3 big data platform that ingests on-chain transactions, trading behavior, and user profiles, then layer AI/ML tools for fraud detection, risk modeling, and natural-language data querying.
Design and build real-time data pipelines with Apache Flink, collaborate on feature stores for ML models, and partner with AI, risk, and compliance teams to keep crypto exchange systems fast and reliable.
Build and optimize LLM-based trading tools like Q&A systems and agents using Python, prompt engineering, and transformer models for a crypto derivatives exchange.
Deploys and maintains AI/ML models, builds data pipelines, and integrates solutions using Python, TensorFlow/PyTorch, and cloud AI services.
Build and lead a real-time risk control platform for crypto payments, integrating fraud detection, credit scoring, and compliance using Java, microservices, and big-data tech.
Builds AI-augmented developer tools using .NET, Python, and cloud services, integrating LLMs and vector databases to speed up software delivery and improve workflows.
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