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Build and deploy cloud data pipelines, model data, and integrate LLM components for enterprise clients while auditing cloud architectures and automating deployments.
Build and deploy data pipelines, cloud data platforms, and ML systems for clients using Databricks, Spark, AWS, and MLOps tooling.
Build robust data pipelines and cloud-native platforms using Python, SQL, dbt, Airflow, BigQuery, Snowflake, and Databricks, while integrating AI/ML workflows and RAG systems.
Build and optimize data pipelines on AWS for clients, focusing on automation, Spark, and CI/CD to enable scalable analytics and business insights.
Consultant Data Engineer designs and optimizes Databricks/Spark pipelines, reduces data-processing costs, and applies LLM tools to improve performance and documentation for a Lille-based client’s data-architecture overhaul.
Build and maintain robust data pipelines and cloud data platforms, preparing data for AI systems and ensuring quality and governance for analytics and agentic use cases.
Design and build scalable data pipelines, ETL/ELT workflows, and cloud data architectures for clients, ensuring data quality and security while enabling real-time analytics and digital transformation.
Build and optimize AWS data pipelines for enterprise clients, focusing on automation, cloud-native tooling (Spark, Airflow, Terraform), and scalable data platforms to enable analytics and business insights.
Build AI-driven applications end-to-end using Python/FastAPI, Supabase, and TypeScript, leveraging tools like Cursor and prompt engineering to architect robust, scalable solutions for enterprise clients.
Build and scale the backend API, database, and mobile app using Node.js, PostgreSQL, and Flutter; integrate cloud services and AI APIs while improving performance and reliability.
Build high-performance backends in Python/FastAPI and integrate Anthropic LLMs with RAG pipelines for a company focused on AI-powered developer tools.
Build and scale the backend of an AI-powered language-learning platform using Python, PostgreSQL, FastAPI, and modern LLMs; integrate AI coding agents into the development workflow.
Build and deploy generative-AI backends in Python, designing RAG pipelines, AI agents, and scalable cloud infrastructure on GCP while owning CI/CD, Docker/Kubernetes, and IaC.
Build a secure, tenant-isolated AI assistant for real estate title/settlement using Vue.js frontend and Python/FastAPI backend on AWS, integrating vector search and LLM APIs.
Build and run backend systems that orchestrate AI models into reliable, low-latency APIs for a proactive smart assistant, handling multi-step workflows and tool calls.
Build and run the backend systems that power an AI chat app, focusing on inference pipelines, orchestration, and reliable APIs for real-world task completion.
Build and maintain backend systems powering Grafana Cloud’s stack lifecycle, including billing, provisioning, and reconciliation for customer stacks across cloud providers.
Build and refactor Grafana’s backend to support multi-tenant SaaS and a unified observability app platform, using Go and collaborating across teams.
Build backend services for Grafana’s AI-native data intelligence system, designing ingestion, storage, retrieval APIs, and agent integrations to power enterprise observability.
Build and operate Pyroscope, an open-source continuous profiling database, designing ingestion, storage, and query systems in Go to improve performance, cost, and integration with Grafana’s observability stack.
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