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Build and maintain AI agent toolkits that let models securely take real actions in enterprise systems, integrating vendor APIs and shaping tool descriptions for reliable agent use.
Build AI-powered analytics tools for Ford’s mobility data, using full-stack development, GCP/Terraform, and LLM-driven agentic workflows to automate industrial decision-making.
Designs and advises clients on secure GenAI or Computer Vision solutions, running discovery workshops and mapping workflows to scalable architectures under high-security constraints.
Build and deploy multi-agent AI systems for global financial markets, using Python, FastAPI, and frameworks like LangGraph and CrewAI.
Build and deploy AI-first contract intelligence features using LLMs, RAG, agentic workflows, and knowledge graphs to automate and accelerate deal-making for enterprise customers.
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.
Lead a team building secure, scalable financial platforms using cloud-native tools and advanced programming to deliver trusted products for JPMorgan Chase.
Build and scale AI agents and reusable components for automation, using Python, LLM APIs, and cloud-native tools to productionize AI systems and optimize performance.
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.
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