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Build real-time data pipelines and vector databases to power AI agents, transforming enterprise logs into embeddings for RAG systems with automated quality guardrails.
Build and scale Snowflake-based data and AI systems, designing pipelines, models, and LLM/RAG applications that turn messy business data into trusted, production-ready solutions.
Build and deploy AI-powered applications for clients, combining full-stack development with consulting to create scalable, production-ready solutions using LLMs, RAG, and cloud infrastructure.
Designs and builds AI-powered solutions using LLMs, RAG, and agentic workflows to improve business processes and customer experiences.
Build robust data pipelines and ML/GenAI solutions for Swiss clients, from ETL to dashboards and predictive models, using Python, SQL, Azure, and Power BI.
Design and build scalable AI/data-science architectures (LLMs, RAG, ML) in cloud and on-prem, then advise customers and implement solutions in Python, Azure ML, and Kubernetes.
Design and maintain AWS-based data pipelines, cloud data platforms, and AI/ML solutions using Python, Spark, and AWS services like S3, Glue, Redshift, and SageMaker.
Builds GenAI applications and autonomous multi-agent systems using Python, LLM APIs, and open-source models for enterprise clients.
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 maintain scalable data pipelines and MLOps infrastructure for industrial AI solutions in mining, energy, and infrastructure sectors, deploying models in cloud/edge environments.
Build and maintain scalable data pipelines and MLOps workflows for industrial AI solutions in mining, energy, and infrastructure, deploying models in cloud/edge environments.
Build and lead cloud-native data pipelines for regulatory reporting in investment banking, migrating legacy systems to AWS and integrating AI-driven workflows using Python, Kafka, and Kubernetes.
Build and maintain production-grade AI systems for document processing and retrieval-augmented generation in a global marketing-tech stack.
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 enterprise-grade AI solutions using data pipelines, cloud platforms, and agent-based systems with LLMs and Vertex AI to deliver scalable, data-driven AI for clients.
Build and ship scalable backend services, APIs, and AI-enabled apps while working directly with clients to solve complex business problems using modern AI tools.
Lead a cloud-native AWS data platform powering GenAI insights and underwriting intelligence, building Python/.NET pipelines, PostgreSQL, OpenSearch, and LangGraph agentic workflows.
Build LLM-powered automations, chat/voice assistants, and RAG pipelines using Python, FastAPI, and vector databases; deploy cloud-native services with CI/CD and guardrails.
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