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Build and deploy production-grade generative AI systems for Capital Group’s investment process, including RAG pipelines, agents, and copilots, while ensuring responsible AI, security, and cost controls.
Разрабатываете и поддерживаете бэкенд AI-агентов на LangGraph, интегрируете модели с внутренними системами банка и внедряете RAG-решения для автоматизации бизнес-процессов.
Build and deploy AI agents that audit code for vulnerabilities, detect malicious prompts, and integrate with DevSecOps pipelines using Python/Go and frameworks like LangChain.
Lead a team building and scaling data pipelines, warehouses, and AI-powered data tools for a global crypto exchange, driving real-time analytics and Agentic AI adoption.
Build and deploy AI systems (LLMs, agentic workflows, RAG) on Azure to power internal knowledge discovery and decision-making at a global nonprofit.
Build and deploy enterprise-grade AI agents using LangChain/CrewAI for client solutions, integrating LLMs, APIs, and cloud platforms while supporting pre-sales demos and solutioning.
Design and lead enterprise AI data architectures, including RAG pipelines, vector indexing, and knowledge graphs, to enable AI agents while enforcing strict data governance and security.
Builds secure, AI-ready data pipelines and retrieval systems for enterprise GenAI agents, focusing on ingestion, normalization, vector search, and governance across tools like SharePoint, Git, and monitoring systems.
Design and build AI agents and workflows using frameworks like LangChain and LangGraph to automate IT service management tasks such as ticket triage and SLA tracking.
Build and deploy production-grade agentic AI systems for enterprise clients, embedding with their teams to design, architect, and ship multi-agent workflows, RAG pipelines, and evaluation harnesses in Python and cloud environments.
Build and deploy custom AI agents for Fortune 500 clients using Salesforce’s Agentforce platform, integrating LLMs and enterprise data pipelines.
Design and build production-grade AI systems, including LLM-based and agentic applications, using Python and modern orchestration frameworks while leading end-to-end delivery for Adobe’s conversational AI solutions.
Build and optimize data pipelines on Databricks, engineer datasets for AI models, and implement retrieval architectures while integrating generative AI capabilities.
Design and lead AI-driven platforms using LLMs and MLOps, ensuring scalable, secure systems aligned with business goals and ethical standards.
Builds and integrates LLMs with ontologies and knowledge graphs to create AI-powered reasoning systems for government clients, using Java, Python, and semantic technologies.
Build scalable data lakehouse pipelines and agentic AI systems that transform unstructured industrial data (CAD, PDF, images) into structured datasets for VLM/LLM training and multi-agent simulations.
Build and lead backend services powering Fitch Ratings’ credit data and ratings platform using Java/Spring Boot, AWS, and modern cloud-native tools.
Design and optimize enterprise RAG platforms using vector databases and semantic search to power AI-driven knowledge systems and integrations with LLMs.
Lead the design and implementation of enterprise-scale generative AI solutions using LLMs and prompt engineering, while mentoring teams and ensuring secure, compliant deployments.
Build Retrieval-Augmented Generation systems that combine LLMs with enterprise knowledge, designing vector search pipelines and document ingestion to deliver accurate, context-aware AI responses.
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