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Build and maintain scalable data pipelines and cloud data platforms for banking clients, using Spark, Scala, and cloud services to deliver clean, governed data for analytics and AI.
Builds the core AI App Builder platform: a full-stack system where users describe ideas and the platform generates functional apps, using React/Next.js, Node.js, and serverless/edge tech.
Build and secure data pipelines for a banking client, using Scala, Spark, and cloud platforms to process large-scale data into reliable, business-ready datasets.
Build and scale production LLM-powered healthcare applications, including RAG pipelines, agentic systems, and evaluation frameworks, while ensuring compliance and reliability in a regulated environment.
Forward-deployed ML engineer partners with clients to deploy Mistral’s AI models, fine-tuning and integrating them into production systems while bridging technical and business stakeholders.
Build and optimize AI-driven automation workflows using Python, n8n, and OpenAI models, integrating systems with PostgreSQL and APIs.
Build and optimize AI-driven automation workflows using Python, JavaScript, and tools like n8n or Zapier, integrating models and APIs for enterprise clients.
Build and tune LLM-based AI agents and RAG systems for ERP automation and SaaS knowledge bases, using Python, PyTorch, LangChain, and cloud pipelines.
Build and deploy enterprise Generative AI apps using LLMs, RAG pipelines, and AI agents with Python, LangChain, and vector databases.
Design and operate scalable data infrastructure for AI model training, migrating legacy systems and ensuring high-performance compute access across cloud and on-premise environments.
Build and maintain the data infrastructure that powers Mistral AI’s large-scale model training and fine-tuning, including compute fleets, storage, and secure MLOps pipelines.
Build and optimize data pipelines on AWS for clients, focusing on automation, Spark, and CI/CD to enable scalable analytics and business insights.
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.
Build and maintain scalable data pipelines and ETL processes to power AI model training and analytics at a cutting-edge AI company.
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 and maintain CI/CD pipelines, cloud infrastructure, and security hardening for enterprise clients using AWS/Azure/GCP, Linux, and DevOps tooling.
Build and maintain cloud-native DevOps pipelines and infrastructure for a portal that unifies cloud products and provides users with a rich dashboard; stack includes GitLab, Terraform, Docker, Kubernetes, Python, Node.js, TypeScript.
Build and maintain CI/CD pipelines and cloud infrastructure for client projects, focusing on automation, security hardening, and DevOps best practices across AWS, Azure, and GCP.
Build and scale a next-gen generative-AI platform using Python, FastAPI, RAG, and LLM integrations (GPT, Mistral, Claude) with cloud-native tooling.
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.
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