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Design, build, and deploy AI-powered applications and agents on Azure to automate workflows and enhance carbon-management solutions for global clients.
Principal AI Engineer designs and owns the shared AI architecture for multi-agent marketing systems, retrieval pipelines, and evaluation frameworks that power SMB-focused products at scale.
Architect and implement generative AI solutions on SAP BTP, designing RAG pipelines, knowledge graphs, and LangChain orchestration to embed LLMs into enterprise SAP applications.
Lead AI engineering for generative-AI copilots and RAG systems in a regulated financial-services environment, building production-grade Python/LLM pipelines on Azure.
Build agentic AI workflows in Python to automate corporate IT reliability tasks like incident triage, root-cause analysis, and remediation, using LLMs, RAG, and agent frameworks.
Lead a team building secure, scalable financial platforms using cloud-native tools and advanced programming to deliver trusted products for JPMorgan Chase.
Build production-ready AI features using Azure OpenAI, RAG pipelines, and MCP agents to enhance SaaS workflows like onboarding and data intake.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, then integrate them into enterprise systems with clean, testable code and CI/CD.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases; implement tools, prompts, and CI/CD while integrating enterprise APIs and ensuring safety.
Builds AI-native applications by implementing LLM tooling, RAG pipelines, and vector search; integrates AI into enterprise systems with Python/TypeScript/Java, frameworks like LangChain, and vector DBs (pgvector, Pinecone).
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating enterprise APIs and ensuring robust testing and safety. Core stack includes Python, TypeScript/Node.js, and Java with frameworks like LangChain and Spring Boot.
Build agentic AI applications using LLMs, RAG pipelines, and vector search; implement tools, prompts, and CI/CD while integrating enterprise APIs and data sources.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases. Develop, test, and integrate agents with enterprise APIs and cloud services.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases. Develop agents, prompts, and integrations with clean code and CI/CD in Python, Java, or TypeScript.
Custom Software Engineer at Accenture in Chennai builds agentic AI applications using LLM tooling, RAG pipelines, vector search, and API integrations, with core techs including Java Full Stack, Python, TypeScript/Node.js, and vector DBs.
Build agentic AI applications using LLMs, RAG pipelines, and vector search, integrating enterprise APIs and data sources into production systems.
Build AI-powered applications using LLMs, RAG pipelines, and vector search, integrating APIs and enterprise systems while owning full-stack development from prototype to production.
Build AI-powered agentic applications by implementing LLM tooling, RAG pipelines, and vector search; integrate with enterprise systems while ensuring scalability, safety, and rapid iteration.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector search, integrating enterprise APIs and ensuring robust, testable code and CI/CD.
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