- 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.

Required Skills

['AI']





Additional Information

- 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 solid 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.

📌 AI Engineer (Hyderabad)
🏢 HuntingCube
📍 Hyderabad