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Job Description Zions Bancorporation’s Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been…
Senior ML Ops Engineer builds and scales production systems for AI-powered grocery recommendations and box personalization using Python, FastAPI, Spark on Databricks, and MLflow on AWS.
Direct message the job poster from Exalto Consulting Exalto consulting are currently recruiting for a contract DevOps Engineer to work Remote for a client in Barcelona, this will be 100% remote working for a global…
Lead high-impact ML projects and set technical standards for a neobank’s Data Science & AI teams, designing credit, fraud, and personalization models at scale.
Build and deploy ML models for cybersecurity use cases like threat detection and risk scoring using Python, PyTorch, and cloud-native pipelines.
Build and scale the ML platform powering PubMatic’s adtech stack, designing pipelines for petabyte-scale data, GPU-accelerated training/inference, and RAG systems to troubleshoot bid streams and deliver AI-driven insights.
Design enterprise-grade data science scenarios and evaluation rubrics for AI systems, focusing on Fortune 500 analytics workflows and tools like Snowflake, Databricks, and MLflow.
Design and deploy enterprise-grade AI agents and ML systems using cloud-native tools (Azure AI Foundry, AWS Bedrock, Google Vertex) and open-source frameworks, ensuring reliability in regulated environments.
Principal AI Data Engineer builds and deploys GenAI, RAG and Agentic AI prototypes using Azure AI Foundry, Databricks Mosaic AI and LangChain on Azure cloud.
Design and deploy production-grade Databricks platforms for enterprise clients, leading end-to-end cloud data engineering projects on AWS/Azure.
Senior Data Engineer builds and optimizes cloud-based data pipelines and warehouses using AWS, Snowflake, Matillion, and dbt to power analytics and AI at Philip Morris International.
Build and maintain scalable data pipelines in Azure Databricks, design governed data models, and deploy ML solutions to production using PySpark, SQL, and CI/CD.
Build and maintain scalable MLOps pipelines on Databricks and Azure to deploy, monitor, and govern AI models in production using PySpark, GitHub, and MLflow.
Build and ship production-grade agentic AI systems in Python, integrating LLMs with tools, memory, and multi-agent workflows on Azure AI Foundry for real customer use cases.
Build and deploy production-grade machine learning systems for ad-targeting and analytics using Databricks, MLFlow, and cloud infrastructure.
Maintain and improve a live credit-scoring ML pipeline: monitor model stability, retrain models, automate workflows, and report on performance metrics like Gini and KS.
Design and integrate GenAI/agentic AI solutions (RAG, MCP, workflows) with developer platforms and automotive middleware, using Python, LLM frameworks, and MLOps tooling.
Lead a greenfield Applied AI team to build production-grade ML and LLM-augmented models for wealth-management workflows, owning the full lifecycle from problem framing to deployment and monitoring.
Build and deploy ML models for wealth management, focusing on causal inference, forecasting, and generative AI workflows using Python, TensorFlow, and LLM APIs.
Principal AI Data Engineer prototypes AI, GenAI and Agentic AI solutions using Azure AI Foundry, Copilot Studio, Databricks Mosaic AI and MLflow, with a focus on RAG pipelines.
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