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Designs and leads enterprise-scale cloud data platforms and AI systems for an airline, focusing on Azure, GenAI, and modern data architecture.
Build, deploy, and monitor ML/AI models and GenAI systems using Python, TensorFlow, PyTorch, and MLOps tooling like MLflow and Kubernetes.
Lead the design and deployment of advanced AI models, including LLMs and multimodal systems, using deep learning and statistical methods to solve complex business and research challenges.
Build and deploy AI/ML models on Databricks to analyze Miral Destinations data and deliver actionable insights for business decisions.
Build and secure Azure landing zones, CI/CD pipelines, and MLOps/LLMOps automation to safely deploy AI agents and apps across sandbox, staging, and production environments.
Build and operate sovereign AI infrastructure for government clients, deploying GPU clusters in air-gapped data centers and cloud (Azure/GCP) using Kubernetes, GitOps, and offline artifact pipelines.
Build and maintain Databricks-based pipelines for anomaly detection and KPI monitoring, using Azure, MLflow, and streaming/batch processing to support predictive maintenance in a climate-tech context.
Build and deploy generative AI chatbots, predictive models, and analytics dashboards using Python, LLMs, and tools like Streamlit and Power BI.
Build and deploy AI systems including LLMs, computer vision, and autonomous agents using Python, PyTorch, and LangChain, then productionize them with MLOps on cloud platforms.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Lead end-to-end training of large language models using domain-adaptive pretraining, fine-tuning, and reinforcement learning, while building robust data and evaluation pipelines for intelligent agent systems.
Senior AI Engineer builds and operates a multi-agent GenAI system with retrieval, knowledge ingestion, and evaluation pipelines, integrating model APIs and cloud infrastructure.
Design and build scalable data pipelines and lakehouse architectures using Microsoft Fabric, Azure Databricks, and Spark to enable robust analytics and reporting for a global packaging leader.
Builds enterprise-grade Python/Django and FastAPI backends plus React/Next.js frontends, deploys on AWS, and integrates AI models (OpenAI, LangChain) into scalable microservices.
Design and build Prospera AI’s data infrastructure from scratch, including Snowflake warehouse, ETL pipelines with dbt/Airflow, and ML feature stores to power analytics and AI model training.
Build and maintain a cloud-agnostic data and ML platform for scalable, reproducible model training and deployment across multiple products, ensuring reliability, cost-efficiency, and EU compliance.
Build and scale a cloud-native data platform using Databricks, Spark, and AWS to power analytics and ML at a data-driven tech company.
Build and maintain AI-powered data pipelines and infrastructure for document automation, deploying LLM agents and ensuring scalable, reliable AI systems in production.
Senior data engineer builds and optimizes cloud-based data pipelines in Azure for a top Amsterdam sports club, using Python, SQL, PySpark, and Databricks to power data-driven decisions.
Senior Data Engineer builds and optimizes scalable Azure/Databricks data and ML pipelines for finance and public-sector clients, using PySpark, MLflow, dbt and CI/CD.
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