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Build and optimize core search and retrieval infrastructure for Pinecone’s vector database, enabling scalable, high-quality AI applications with semantic and hybrid search capabilities.
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.
Designs and implements AI/ML and generative AI solutions for enterprise clients, focusing on architecture, LLM/RAG pipelines, and MLOps—collaborating with data and product teams to scale AI from proof-of-concept to production while ensuring security, governance, and cloud platform integration.
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.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases; implement agents, prompts, and evaluation loops with Python/TypeScript/Java.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases; develop scalable agentic systems with Python/TypeScript/Java and modern frameworks.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating APIs and cloud services while owning end-to-end development and testing.
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