Artificial Intelligence Engineer

Job description

• · Strong understanding of agentic AI fundamentals—goal definition, planning, reasoning, task decomposition, and autonomous action loops.
• · Design and build agentic workflows using LLMs, including tool/function calling, state management, memory, and orchestration patterns (single-agent/multi-agent).
• · Hands-on experience with agent frameworks and patterns (e.g., LangChain-based agent frameworks or equivalents), including routing, workflows, and agent coordination.
• · Model Context Protocol (MCP) proficiency for integrating tools and contextual data into agent workflows (tool discovery/usage via MCP servers).
• · Build Retrieval-Augmented Generation (RAG) pipelines and gr ounding strategies for enterprise knowledge sources (e.g., internal repos/wikis/ticketing/content stores) to improve agent reliability.
• · Agent observability & monitoring (AgentOps): track agent behavior, tool calls, outcomes, and quality signals; implement alerts/traces and feedback loops for continuous improvement.
• · Cloud and/or platform engineering exposure for deploying agentic systems at scale (containers/Kubernetes/OpenShift, CI/CD, performance tuning, and secure operations).