Description Summary: Role Overview As part ofProduct work cross-functionally to translate data science prototypes into robust, production-ready applications; and ensure seamless integration of AI components with existing enterprise data platforms and business systems Implement AI observability and telemetry: implement runtime observability for AI agents and LLM applications, including tracing, reasoning paths, token consumption, latency, cost, output quality, and guardrail violations to ensure production reliability Build agent evaluation frameworks: design agent evaluation pipelines, develop evaluation harnesses, benchmark datasets, regression tests, and automated quality scoring to continuously assess agent accuracy, safety, and business performance Engineer enterprise AI context: design retrieval pipelines using enterprise semantic layers, knowledge graphs, vector search, and business ontologies to ground AI agents in trusted enterprise context and improve response quality Implement AI safety and runtime controls: configure runtime AI controls including policy enforcement, human-in-the-loop workflows, autonomy thresholds, prompt injection defenses, and secure tool execution for enterprise AI agents Design multi-agent systems: design and orchestrate multi-agent systems that coordinate planning, reasoning, tool execution,



and human collaboration across complex enterprise workflows Operate AI applications in production: manage prompt versioning, evaluation, experimentation, routing strategies, cost optimization, and the production lifecycle for LLM- and agent-powered applications using modernLLMOpsandAgentOpspractices Support digital twin capabilities: develop AI capabilities that support enterprise digital twins by integrating operational, commercial, and enterprise data into intelligent simulations, predictions, and decision-support workflows Collaborate effectively within agile engineering teams: work closely with Technical Leads, software engineers, data engineers, and fellow AI Engineers to design, build, test, and deliver AI capabilities.
Contribute to sprint planning, backlog refinement, technical design discussions, code reviews, and collaborative problem-solving to ensure high-quality engineering outcomes Maintain technical currency and drive continuous improvement: stay current with advances in AI, machine learning, and Generative AI and integrate relevant developments into existing and new solutions; conduct rigorous testing and validation to ensure reliability, accuracy, and explainability of AI agents and outputs; and contribute to internal knowledge sharing, code reviews, and engineering best practices Requirements

📌 Senior Manager, Ai Engineer (Xico)
🏢 Coca Cola
📍 Xico