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Build and maintain a production platform that collects, processes, and enriches social media content using Python, ETL pipelines, and LLM APIs for classification and summarization.
Act as a technical advisor during pre-sales, designing AI/LLM and modern data platform solutions for enterprises, and guiding clients from discovery to implementation.
Build production-ready GenAI and agentic AI systems for global enterprises using Python, LLM APIs, and cloud platforms like AWS/Azure/GCP.
Build enterprise-grade GenAI applications and autonomous multi-agent systems using Python, LLM APIs, and vector databases, then deploy them to cloud platforms for Fortune 500 clients.
Build and deploy AI-powered safety systems: liveness detection, fraud/abuse detection, audio analysis, and safety scoring using CV, NLP, and LLM pipelines in a high-load production environment.
Senior backend engineer building AI-powered sports data services and LLM integrations for high-traffic fan platforms using C# .NET, Python, SQL, AWS, and Snowflake.
Build and maintain AI-powered search infrastructure: ETL pipelines, embedding generation, document processing, and ranking systems to power semantic search and AI agent queries.
Build and maintain AI-powered search infrastructure: ETL pipelines, semantic embeddings, document processing, and retrieval systems to power accurate AI responses.
Design and build scalable cloud-native AI data platforms and pipelines using Python, Spark, and Kafka to power machine learning and GenAI initiatives for enterprise clients.
Build and operate Snowflake-based data platforms that power GenAI and advanced AI use cases at enterprise scale, integrating vector search, RAG, and agent workflows.
Build and maintain scalable data pipelines and Lakehouse architectures using Databricks, PySpark, and Delta Lake to power analytics and GenAI solutions for enterprise clients.
Build and curate insurance data assets, integrate GenAI for data quality, and deliver Power BI dashboards to support underwriting and claims decisions.
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 data pipelines and Lakehouse architectures using Databricks, PySpark, and Delta Lake to deliver analytics-ready datasets for GenAI and business use cases.
Build and automate predictive models, ETL pipelines, and monitoring systems using Python, Databricks, and Azure to power AI-driven marketing analytics and campaign optimization.
Build and maintain data pipelines and ETL processes to support AI agents and ML workflows, ensuring reliability, security, and compliance for healthcare-focused AI initiatives.
Build and maintain cloud-based ELT/ETL pipelines, orchestrate Docker/Kubernetes deployments, and automate CI/CD for a global scientific intelligence team using Python, SQL, and AWS/Azure.
Build and maintain scalable data pipelines and AI-ready cloud infrastructure using Python, PySpark, and cloud platforms to support analytics and machine learning workflows.
Build and evolve Roche’s internal AI Agentic Platform, designing scalable agentic workflows, reusable components, and enterprise integrations for secure, compliant AI solutions in healthcare.
Build and maintain scalable data pipelines, cloud data infrastructure, and AI/ML-ready datasets for a global consulting firm.
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