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Design and build scalable data pipelines and platforms to power analytics and AI solutions using cloud technologies like AWS, Databricks, and Snowflake.
Build and maintain scalable data pipelines in Python and AWS to feed AI platforms and BI tools, ensuring high-quality data for analytics and decision-making.
Design and build scalable cloud data pipelines and lakehouse architectures for a government project, using Python, Spark, Kafka, and AWS services.
Design and build AI-ready data platforms and ML pipelines for analytics, GenAI, and RAG systems on AWS/Azure/GCP, ensuring production-grade delivery and MLOps practices.
Entry-level AI Data Engineer builds cloud-native data pipelines and automation for cybersecurity SOC operations using AWS, Python, and IaC tools like Terraform.
Design and build scalable cloud-native data pipelines and lakehouse architectures, integrating AI/ML capabilities for a government data platform using Python, Spark, Kafka, and AWS services.
Builds and maintains scalable backend services in Go/Python and integrates ML models into production for AI/LLM-powered products, while also developing React-based UIs.
Build and deploy ML models (churn, pricing, fraud) and LLM-based agents on AWS SageMaker to automate insurance workflows and support data-driven decisions across the business.
Build and automate data pipelines for financial clients, migrating on-prem systems to cloud and implementing DevSecOps practices using Kafka, Spark, Kubernetes, and CI/CD tools.
Build and automate scalable MLOps platforms and AI infrastructure, deploying ML pipelines on Kubernetes and cloud-native environments to support data scientists and AI engineers.
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 and maintain ETL pipelines, dashboards, and AI agents to support renewable-energy operations, using SQL, Python, and BI tools like Tableau or PowerBI.
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.
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 cloud-native AI and web apps using AWS, React, NextJS, Java, NodeJS, and Python; collaborate with architects on scalable microservices.
Build and maintain Snowflake data models to power AI-driven marketing systems, enabling agentic workflows and LLM tool-calling for predictive and generative use cases.
Design and build scalable AWS data pipelines and real-time streaming systems using Confluent Kafka for analytics and AI workloads.
Build and scale a cloud-native data platform on AWS to power real-time recommendations and analytics for Europe’s leading brands.
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