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Lead Agentic AI Engineer
Accenture in India
Kochi, IND
Full-time
Mid-Senior
Onsite
Discovered 1 weeks ago
Agentic AILLM application developmentAWS Bedrock, Azure AI Foundry, or GCP Vertex AIPythonBackend development or data engineeringMCP
Free
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Agentic AILLM application developmentAWS Bedrock, Azure AI Foundry, or GCP Vertex AI
Role Overview
Build AI agents, LLM-powered applications, and intelligent workflow automation solutions using AWS Bedrock, Azure AI Foundry, or GCP Vertex AI.
Design agent-based systems that reason, plan, retrieve information, use tools, communicate with other agents, and automate complex workflows.
Deliver scalable, secure, interoperable, and production-ready AI solutions with architects, product teams, data engineers, and application developers.
Key Skills for This Role
Agentic AILLM application developmentAWS Bedrock, Azure AI Foundry, or GCP Vertex AIPythonBackend development or data engineeringMCP
Full Job Posting
Job Summary
Build AI agents, LLM-powered applications, and intelligent workflow automation solutions using AWS Bedrock, Azure AI Foundry, or GCP Vertex AI.
Design agent-based systems that reason, plan, retrieve information, use tools, communicate with other agents, and automate complex workflows.
Deliver scalable, secure, interoperable, and production-ready AI solutions with architects, product teams, data engineers, and application developers.
Roles and Responsibilities
- Build and support production-grade Agentic AI solutions on major cloud AI platforms.
- Design agents for tool use, workflow orchestration, RAG, reasoning, task automation, memory management, and multi-agent collaboration.
- Create scalable APIs, data pipelines, integrations, agent tools, and AI-enabled applications.
- Use MCP and A2A patterns to support secure tool connectivity, context access, agent discovery, task delegation, and collaboration.
- Develop backend services, connectors, and integrations for enterprise systems, databases, documents, and external tools.
- Implement RAG using vector databases, embeddings, document processing, semantic search, and knowledge retrieval.
- Evaluate and improve prompt quality, accuracy, latency, reliability, guardrails, security, and cost efficiency.
- Debug agent behavior, integrations, tool execution, MCP and A2A flows, retrieval, workflows, and production deployments.
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