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Design and lead enterprise-grade AI/ML architectures for DHL’s logistics systems, including generative AI, MLOps, and cloud platforms like Azure and Google Cloud.
Designs and maintains cloud infrastructure and CI/CD pipelines to deploy and operate AI models in production, collaborating with AI teams and automating ML workflows.
Build and deploy AI models in Python using TensorFlow/PyTorch, then productionize them with MLOps on Azure and Docker; translate insights into business decisions.
Architect enterprise-grade data and AI solutions for an insurer, designing cloud-native pipelines, Databricks lakehouses, and Agentic AI systems while aligning with insurance domains like IFRS17 and actuarial needs.
Build and deploy AI-powered applications on AWS, integrating LLMs and cloud-native tech to create production-ready AI services and agents.
Design and deploy ML systems and predictive models using Azure MLOps and NLP for client projects.
Design, build, and deploy AI/ML models and Generative AI solutions for clients in banking, healthcare, and other industries, covering the full lifecycle from data prep to MLOps.
Designs, builds, and deploys ML and generative AI solutions for banking and healthcare clients, owning the full lifecycle from data exploration to production deployment.
Build and deploy scalable AI/ML models and LLM-based systems on Hilti’s AI platform, using Python, Azure, and MLOps/LLMOps practices to power global construction workflows.
Lead a team to build and deliver enterprise data platforms using Snowflake, BI tools, and data engineering pipelines for a global chemical manufacturer.
Build and maintain an AI platform: design services, deploy LLMs, optimize inference, and implement MLOps/RAG workflows for enterprise AI solutions.
Designs and leads enterprise-grade data and AI platforms for an insurer, focusing on scalable, governed solutions on Azure and GCP with MLOps and Databricks governance.
Build and deploy ML systems using LLMOps, deep learning, and Azure for predictive analytics and AI solutions for enterprise clients.
Senior Data Engineer builds and maintains scalable data pipelines and governed data products for enterprise use cases, collaborating with cross-functional teams.
Build and maintain scalable data pipelines and curated data assets for agency servicing use cases, ensuring reliability, auditability, and governance while collaborating with cross-functional teams.
Designs and automates MLOps and DevOps pipelines for scalable AI/ML platforms, ensuring secure, reliable model training and inference workflows across multi-cloud and on-premises environments.
Build and optimize AI applications and MLOps pipelines using Python, LLMs, and cloud platforms while collaborating with data scientists and architects.
Build, integrate, and optimize AI/ML solutions using Python, LLMs, and MLOps pipelines for enterprise clients, while collaborating with data scientists and engineers.
Build and maintain AI-driven data pipelines and MLOps infrastructure in Python, leveraging vector databases and cloud platforms like AWS or Azure.
Builds and maintains scalable data pipelines and contributes to LLM-driven BI applications using AI/ML.
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