AI Engineer

HuntingCube; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

HuntingCube in Hyderabad is seeking an experienced AI/ML engineer to design, develop, and deploy AI-powered applications for enterprise use cases, including multi-agent systems and AI workflows.

Responsibilities include building production-grade LLM-powered applications, integrating with Claude Agent SDK and AWS Bedrock, and designing robust RAG architectures with secure tool execution. You will collaborate with engineering teams to embed AI capabilities into products.

Qualifications

• Design AI-powered applications for enterprise use cases.
• Build multi-agent systems and AI workflows.
• Develop production-grade LLM-powered apps including chatbots and copilots.
• Design enterprise-grade RAG architectures with retrieval, embedding, ranking, and context management.

Responsibilities

• Design, develop, and deploy AI-powered applications and intelligent automation systems for enterprise use cases.
• Build and orchestrate multi-agent systems, autonomous agents, tool-using agents, and AI workflows.
• Develop production-grade LLM-powered applications, including conversational AI, chatbots, copilots, and AI automation.
• Design and implement enterprise-grade RAG architectures using appropriate retrieval, embedding, ranking, and context-management strategies.
• Integrate LLMs and AI capabilities using provider SDKs such as Claude Agent SDK and other leading LLM platforms.
• Build AI solutions using cloud-native AI services, with strong preference for AWS Bedrock experience.
• Develop MCP (Model Context Protocol) integrations to connect AI agents with enterprise tools, APIs, databases, and external systems.
• Design robust tool-calling and function-calling architectures for AI agents.
• Build reusable frameworks for agent orchestration, memory, context management, evaluation, and observability.
• Implement AI security and safety frameworks, including access control, data protection, prompt-injection mitigation, guardrails, and secure tool execution.
• Work with engineering teams to integrate AI capabilities into existing enterprise products and workflows.
• Evaluate LLM models, prompts, agents, and retrieval strategies based on accuracy, latency, reliability, and cost.
• Develop automated evaluation and monitoring mechanisms for LLM and agentic AI systems.
• Write clean, scalable, maintainable, and production-ready code.
• Participate in architecture discussions, technical design reviews, code reviews, and engineering best practices.
• Mentor engineers and contribute to building strong AI engineering practices within the organization.

Skills

AI design

LLM integration

Agent orchestration

Cloud-native AI

Tools

Claude Agent SDK

AWS Bedrock

LLM platforms

Job description

Job Description

• Design, develop, and deploy AI-powered applications and intelligent automation systems for enterprise use cases.


• Build and orchestrate multi-agent systems, autonomous agents, tool-using agents, and AI workflows.


• Develop production-grade LLM-powered applications, including conversational AI, chatbots, copilots, and AI automation.


• Design and implement enterprise-grade RAG architectures using appropriate retrieval, embedding, ranking, and context-management strategies.


• Integrate LLMs and AI capabilities using provider SDKs such as Claude Agent SDK and other leading LLM platforms.


• Build AI solutions using cloud-native AI services, with strong preference for AWS Bedrock experience.


• Develop MCP (Model Context Protocol) integrations to connect AI agents with enterprise tools, APIs, databases, and external systems.


• Design robust tool-calling and function-calling architectures for AI agents.


• Build reusable frameworks for agent orchestration, memory, context management, evaluation, and observability.


• Implement AI security and safety frameworks, including access control, data protection, prompt-injection mitigation, guardrails, and secure tool execution.


• Work with engineering teams to integrate AI capabilities into existing enterprise products and workflows.


• Evaluate LLM models, prompts, agents, and retrieval strategies based on accuracy, latency, reliability, and cost.


• Develop automated evaluation and monitoring mechanisms for LLM and agentic AI systems.


• Write clean, scalable, maintainable, and production-ready code.


• Participate in architecture discussions, technical design reviews, code reviews, and engineering best practices.


• Mentor engineers and contribute to building strong AI engineering practices within the organization.