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You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The…
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.
Builds and deploys production-grade agentic AI systems (LLMs, RAG, orchestration) for American Express’s global commercial services, integrating with cloud-native stacks (Go/TypeScript/Python, AWS/GCP, Kafka, Kubernetes) to enhance customer-facing financial solutions.
Build production-ready AI features using Azure OpenAI, RAG pipelines, and MCP agents to enhance SaaS workflows like onboarding and data intake.
Build and deploy AI agents for Adobe Express, using LLMs, RAG, and multi-agent systems to power creative workflows and scale production systems.
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.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases; implement agents, prompts, and evaluation loops while integrating enterprise APIs and ensuring robust CI/CD.
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