Artificial Intelligence Engineer

Role & responsibilities

Agentic AI Engineer

Preferred candidate profile

• 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 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).