Agentic AI Engineer

Job description

- Degree in computer science, artificial intelligence, or IT. - Strong understanding of agentic AI fundamentalsgoal 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 grounding 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). - Responsible AI & guardrails: design human-in-the-loop controls, safe tool access, and security-aware policies for agents operating with credentials, data access, and budgets. - Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).