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Build and maintain ETL pipelines in Python to move and transform data, using SQL and AWS services like S3 and Athena.
Build and maintain ML models and data pipelines using Python, PySpark, and AWS SageMaker for advanced analytics projects.
Design and deploy ML models and LLM-based solutions for an insurer, build robust data pipelines in AWS SageMaker, and translate insights for stakeholders.
Build and run the AIOps platform that keeps AI models, LLM pipelines, and agents reliable, scalable, and cost-efficient in AWS/Azure.
Build and scale AI-driven products using LLMs, RAG pipelines, and vector databases with Rust or Golang, from prototype to production in rapid cycles.
Design and automate scalable SageMaker environments and CI/CD pipelines for ML model deployment across Europe using AWS, Python, and Jenkins.
Contract DevOps Engineer builds and maintains AWS-based CI/CD pipelines, Kubernetes clusters, and observability for a global SaaS platform using ArgoCD, GitHub Actions, and Splunk.
Build and run the cloud platform that powers the company’s AI services on AWS and Kubernetes, automating deployments with Terraform and CI/CD while supporting MLOps workflows like SageMaker, MLflow, Airflow and Kubeflow.
Designs and automates AWS cloud infrastructure with DevSecOps practices, deploys AI/ML platforms on Bedrock/SageMaker, and manages EKS/ECS for scalable, secure solutions.
Maintains and scales AWS cloud infrastructure for data analytics projects, automates deployments with CI/CD, and ensures security and cost efficiency.
Build and deploy ML models and MLOps pipelines using Python, TensorFlow/PyTorch, and cloud platforms like Azure Databricks for real-time, event-driven data solutions across industries.
Design and build scalable data pipelines and platforms (Lakehouse, Data Warehouse) to feed ML models and generative-AI applications, using Python, Spark, Snowflake/Databricks, and cloud stacks (AWS/GCP/Azure).
Design and maintain AWS-based data pipelines, cloud data platforms, and AI/ML solutions using Python, Spark, and AWS services like S3, Glue, Redshift, and SageMaker.
Act as a technical advisor during pre-sales, designing AI/LLM and modern data platform solutions for enterprises, and guiding clients from discovery to implementation.
Build and maintain AWS-based data pipelines and ML workflows, turning datasets into insights and scalable analytics for clients across industries.
Build and maintain data pipelines and infrastructure for AI solutions, working with Spark, Python, and LLMs to support machine learning models and predictive analytics.
Build and own scalable AI/ML data pipelines and real-time streaming systems using Python, Spark, Airflow, Kafka, and cloud services like SageMaker and Snowflake.
Senior Data Engineer/ML Engineer builds and deploys ML models and pipelines in a banking context using Python, Spark, and AWS services like SageMaker and Glue.
Senior Data Engineer builds and maintains ETL/ELT pipelines, data models, and quality checks using AWS, Spark, Python, and Databricks to deliver clean data for analytics and ML teams.
Senior Data Engineer builds and maintains AWS-based ETL pipelines, transforming raw data into clean, analytics-ready datasets for product, ML, and analytics teams.
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