AI Engineer

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.

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 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.