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Principal engineer defining and building next-gen AI agent platforms on Oracle Cloud Infrastructure, leading multi-team execution and hands-on design of scalable, secure, and cost-aware agentic systems.
Build and deploy generative AI models (LLMs, RAG, prompt engineering) on Azure to solve financial-services problems like health analytics and operations modernization.
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.
Build LLM-powered agents to automate code generation, testing, reviews, and deployment, integrating AI into Lattice’s software toolchain.
Principal Data Engineer designs and builds scalable cloud data pipelines using Snowflake, Databricks, and AWS, while integrating AI agents for automated data quality and transformation workflows.
Build and tune LLM-based AI agents and RAG systems for ERP automation and SaaS knowledge bases, using Python, PyTorch, LangChain, and cloud pipelines.
Design and build AI-agent workflows, integrating models, memory, and tools; set reliability standards and evaluation tooling for production systems.
Build and scale an AI agent platform for a fintech company, integrating Python/FastAPI backends with React/TypeScript frontends to automate workflows in a regulated environment.
Designs and deploys production-grade AI systems, including multi-agent workflows and RAG, on GCP while mentoring engineers and enforcing engineering standards.
Build and validate agentic AI features for a patent-intelligence platform using NLP, LLMs, and retrieval strategies to power natural-language patent analysis workflows.
Build LLM-based agents and RAG systems for autonomous network operations, integrating fault diagnosis, predictive analytics, and closed-loop decision support using Python, LangChain, and vector/graph databases.
Build and deploy enterprise Generative AI apps using LLMs, RAG pipelines, and AI agents with Python, LangChain, and vector databases.
Lead AI Engineer designs, builds, and deploys enterprise-scale AI/ML and Generative AI systems, including RAG pipelines and agentic workflows, across Azure, GCP, or AWS.
Сегодня AI-агенты на базе LLM являются одной из самых передовых и востребованных технологий на рынке цифровых продуктов. Сбер как большая технологическая компания, одна из немногих в России обладающая собственной LLM,…
Backend Engineer builds scalable web apps and AI-driven agent workflows using Python, LangChain/LangGraph, and RAG pipelines to deliver intelligent agricultural solutions.
Builds scalable backend services and agentic workflows for Syngenta’s AI-powered agriculture platform, integrating LLMs with RAG pipelines and Python microservices.
Build and maintain scalable backend services and agentic workflows for AI-powered agricultural tools, using Python, LangChain/LangGraph, and RAG pipelines.
Builds and deploys LLM-powered features (agents, RAG, tools) with React/TypeScript frontends and Python or Go backends, integrating vector stores and agent frameworks.
Design and build scalable GCP data pipelines and deploy agentic AI systems using Vertex AI, LangChain, and RAG for enterprise clients.
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.
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