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Own AI-focused data platform features from PRFAQ to launch, partnering with engineering and GTM to ship vector search, RAG, and agent frameworks for enterprise customers.
Architect and build high-throughput, low-latency FastAPI microservices for a cloud-native backend, using async Python, PostgreSQL, Redis, and Kubernetes.
Senior Data Engineer builds and scales Python-based ELT/ETL pipelines, data warehouses, and DataOps practices for high-growth client products using tools like dbt, Airflow, and Snowflake.
Build and scale the backend platform that powers AI-driven travel experiences, integrating LLMs, vector search, and agentic systems for millions of users.
Build and optimize core search and retrieval infrastructure for Pinecone's vector database, enabling scalable, high-quality AI applications with semantic and hybrid search, indexing pipelines, and retrieval orchestration.
Design and build scalable data pipelines using Databricks, Snowflake, and Spark, leading architecture decisions and optimizing performance across AWS/GCP/Azure for client projects.
Develops AI/ML solutions for warehouse design automation, working with LLMs, RAG pipelines, and integrating models via FastAPI. Core technologies include Python, PyTorch, TensorFlow, Docker, and cloud infrastructure on Yandex Cloud.
Design enterprise AI solutions using generative agents, RAG, and ITSM/AIOps workflows to automate IT support, diagnostics, and operations with human-in-the-loop oversight.
Build and maintain AI-ready data pipelines and platforms for a global real-estate operator, integrating property, financial, and IoT data to power analytics and LLM tools across the business.
Builds and optimizes multi-agent AI systems, RAG pipelines, and LLM integrations, focusing on production-grade prompt engineering, evaluation, and deployment.
Build and maintain full-stack web apps in PHP (Laravel/Symfony), Python and TypeScript, integrating AI features (LLM APIs, vector DBs) and optimizing performance, security and scalability.
Build and deploy generative AI solutions using RAG, prompt engineering, and frameworks like LangChain on Google Cloud or Azure.
Build and maintain the internal AI platform that powers AI features across MeridianLink’s products, including model serving, RAG infrastructure, prompt management, and evaluation pipelines.
Build, deploy, and scale AI applications using GenAI, LLMs, RAG, and classic ML on Google Cloud and Azure, while automating data pipelines and model monitoring for enterprise clients.
Design and deliver production-ready AI solutions for enterprises, including generative AI, RAG, and agentic workflows, while guiding clients from architecture to deployment.
Build and scale production LLM and agentic systems for a healthcare specialty-care platform, including RAG pipelines, multi-agent workflows, and compliance-grade AI in Azure.
Build and scale production-grade LLM and agentic systems for a healthcare specialty-care platform, including RAG pipelines, multi-agent workflows, and compliance-ready AI in Azure.
Build and deploy AI agents using LLMs, orchestration frameworks, and RAG pipelines to automate multi-step tasks for Morgan Stanley’s financial services platform.
Build and deploy AI-agent systems for enterprise clients, owning the full lifecycle from discovery to production using Python, FastAPI, and agent frameworks like Strands.
Build and deploy generative-AI solutions using LLMs, prompt engineering, fine-tuning, embeddings, and RAG pipelines for enterprise use cases like customer service and document automation.
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