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Build and deploy ML models on Azure Databricks to analyze healthcare data, engineer features, and deliver predictive analytics for business insights.
Designs, builds, and deploys production-grade ML systems (LLMs, pipelines, and automation tools) for a mid-market professional services firm’s AI-driven internal workflows, collaborating with data scientists, engineers, and stakeholders.
Build and maintain the infrastructure and pipelines that deploy, monitor, and scale machine-learning models in production, using Docker, Kubernetes, and cloud ML platforms.
Lead the design and deployment of AI/ML models and advanced analytics to solve business challenges, mentor a data-science team, and drive enterprise-wide AI adoption.
Design and deploy ML models using Python, TensorFlow/PyTorch, and MLOps tools; collaborate with data scientists to build scalable AI systems for real-world applications.
Builds and deploys AI/ML systems (classical models, LLMs, agentic workflows) in production using Databricks, Azure ML, and Kubernetes, with a focus on forecasting, prompt engineering, and observability.
Build and operate the production ML/LLM platform for healthcare workflows, including training, deployment, monitoring, and compliance systems on GCP.
Design and build scalable data pipelines, ML workflows, and governance frameworks on Azure to enable analytics and AI across an insurance company.
Design and automate tests for AI/ML systems, including model performance, data pipelines, and API integrations, to ensure accuracy and reliability.
Build and maintain scalable data pipelines and cloud data warehouses (Snowflake, AWS) using ETL/ELT tools (Matillion) to support analytics and AI/ML workflows.
Design, build, and deploy AI models (NLP, LLM, RAG) and data solutions end-to-end, from business needs to production, using Python, MLOps, and cloud platforms.
Builds and tests backend data pipelines and ML services for a clinical product, ensuring data quality, reproducibility, and auditability with Python, Django, and AWS.
Builds predictive models and AI tools for national defense, analyzing spatial and operational data to enhance mission outcomes and threat detection.
Lead a team building AI/ML models for spatial intelligence, focusing on anomaly detection, predictive analytics, and agent-based modeling to support defense and intelligence missions using Python, SQL, and cloud-native tools.
Lead the design and optimization of scalable data pipelines and platforms using Hadoop, Databricks, and cloud tech to power enterprise analytics and AI solutions.
Lead the design and build of scalable data pipelines and platforms using Python, PySpark, Hadoop, Databricks, and cloud tools to power analytics, AI/ML, and business intelligence for enterprise clients.
Develops and maintains ML models (tabular and NLP) to detect phone spam and fraud, focusing on feature engineering, pipeline optimization, and collaboration with business stakeholders to improve detection accuracy and latency in production.
Product area Whether it is paying online with Autofill, using tap and pay in stores, or using the Google Pay app, the Payments team at Google is focused on making payments simple, seamless, and secure. In addition to…
Lead the design and deployment of production-grade ML/AI systems using Azure ML, Databricks, and PySpark, ensuring scalable, reproducible, and monitored models in a CI/CD/CT pipeline.
Builds and deploys production ML systems for risk assessment and underwriting in title insurance/real estate, focusing on scalable tabular models, monitoring, and cross-functional collaboration.
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