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Design and build scalable Lakehouse architectures (Bronze/Silver/Gold) using Databricks and Delta Lake to transform logistics data into BI insights, optimizing ETL/ELT pipelines with PySpark and Azure/AWS/GCP services.
Build and maintain scalable data pipelines and Lakehouse models on Databricks, using PySpark, Delta Lake, and Medallion architecture to deliver analytics-ready datasets for enterprise clients.
Build and maintain scalable Azure data pipelines and lakehouse solutions using Databricks, PySpark, and Azure Data Factory to process and transform large datasets for enterprise clients.
Build and maintain scalable Azure data pipelines and Databricks lakehouse solutions, transforming raw data into analytics-ready assets while collaborating with cross-functional teams.
Build and maintain scalable data pipelines and Lakehouse architectures using Databricks, PySpark, and Delta Lake to deliver analytics-ready datasets for GenAI and business use cases.
Build and maintain scalable data pipelines for large enterprises using Spark, Cloudera, and Airflow to process streaming and batch automotive, aerospace, and telecom data.
Build and automate predictive models, ETL pipelines, and monitoring systems using Python, Databricks, and Azure to power AI-driven marketing analytics and campaign optimization.
Builds and maintains dentsu’s Google Cloud-based data platform, writing Python pipelines in Airflow, integrating marketing and semi-structured data, and modeling in BigQuery for analytics and reporting.
Senior Data Engineer builds scalable pipelines to process billions of market data records for trading systems using Python, SQL, Spark, and Kafka.
Senior DevOps/MLOps Engineer builds and scales production-grade ML pipelines for large-scale medical image analysis, using Python, Docker, Kubernetes, and MLOps tools like MLflow.
Build and maintain scalable data pipelines and platforms using Azure, Databricks, PySpark, and Python to enable analytics and AI workloads.
Build and scale production-grade ML pipelines for medical image analysis, deploying models with Kubernetes and MLOps tools like MLflow while collaborating with data scientists and engineers.
Build and maintain the AI/ML platform that turns experimental models into production-grade, governed solutions using Databricks, AWS, MLflow and MLOps practices.
Build and maintain the AI/ML platform that turns experimental models into production-ready, governed, and scalable solutions using Databricks, AWS, MLflow, and related tools.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time microservices, and MLOps tooling using Python, cloud platforms, and frameworks like LangChain.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time model serving, and MLOps tooling in Python and cloud platforms.
Lead a team of data scientists to build ML models that classify and sort recycled plastics in real time using spectral sensor data and deep learning.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time APIs, and MLOps tooling in Python and cloud platforms.
Build and deploy AI/ML pipelines, fine-tune LLMs, and integrate generative AI into banking systems using Python, cloud platforms, and MLOps tools.
Build and scale AI/ML pipelines and GenAI systems for GE HealthCare, automating model deployment, monitoring, and lifecycle management across hybrid/multi-cloud (AWS, Azure).
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