AI Operations Engineer

Job Summary

Meet the Team

Join Ciscos Commerce Intelligence Data Analytics team, a pivotal group delivering seamless intelligence, advanced analytics, and cutting-edge agentic experiences across our business operations. Our mission is to build a scalable, unified, and AI-ready data foundation that drives high-impact business decisions and automated Agentic actions. As a specialized team of engineers and operational experts, we move beyond traditional maintenance to solve complex, large-scale commerce challenges with agentic AI. We achieve this by blending innovation, deep process knowledge, technical expertise, and a relentless focus on business impact, ultimately enhancing operational efficiency and data-driven decision-making at scale.

Your Impact

As an AI Engineer, you will design, build, and operate the autonomous and semi-autonomous agents that power Ciscos commerce operations. You will translate ambiguous operational requirements into reliable multi-step agent workflows - orchestrating LLM reasoning, tool and function calling, retrieval, and integrations into commerce platforms and operational systems. You will be responsible for the full lifecycle: designing the agent and its guardrails, grounding it with retrieval, connecting it to enterprise systems through robust API and agent-to-agent (A2A) integrations, evaluating its behavior, and deploying and monitoring it in production by collaborating with extended team across various time zones. You will partner with cross-functional AI teams to identify high-value opportunities and deliver intelligent, automated workflows that optimize performance and drive durable growth. This role is a pillar to our operational strategy and to Ciscos AI-first vision.

Responsibilities

• Architect and build production AI agents - including multi-agent and human-in-the-loop workflows - that automate commerce operations. Work in partnership and guidance from the core team at onsite
• Implement agent orchestration with modern frameworks (e.g., LangGraph, LangChain, AutoGen, CrewAI, or equivalent), designing robust tool/function-calling, planning, and state management.
• Build agent-to-agent (A2A) integrations and interoperability using emerging standards and protocols (e.g., A2A, MCP), enabling agents to delegate, coordinate, and hand off work reliably.
• Design and implement API integrations - REST/GraphQL, event-driven and webhook-based services, authentication, rate limiting, and resilient error handling - to connect agents with commerce platforms and enterprise data sources.
• Manage the end-to-end lifecycle of LLM-powered applications using Snowflake Cortex, from model selection and fine-tuning to deployment, versioning, and decommissioning.
• Architect robust agentic workflows using Snowflake Native Apps and Stored Procedures, implementing sophisticated tool-calling, planning, and state management that ensures seamless execution across complex operational tasks.
• Build and tune retrieval-augmented generation (RAG) pipelines over documents, structured data, and knowledge repositories. Implement and maintain low-latency vector databases, semantic search architectures, and Retrieval-Augmented Generation (RAG) pipelines using native vector data types and search functions.
• Build automated monitoring mechanisms to track LLM drift, hallucination rates, and token usage, triggering self-healing alerts and automated model retraining or routing workflows.
• Proactively manage and optimize Snowflake credit consumption for AI workloads; implement warehouse auto-scaling, query profiling, and multi-cluster strategies to ensure cost-efficiency at scale.
• Design guardrails and human-in-the-loop gates for sensitive actions, and implement LLM routing and failover for reliability and cost control.
• Apply prompt engineering and context engineering to make agent behavior accurate, safe, and consistent under real operational conditions.
• Own the AI/ML CI/CD pipeline, ensuring rigorous version control, automated testing, and progressive rollout of AI models and agentic workflows across development, staging, and production environments.
• A self-starter with proven expertise to deliver outcomes with minimal supervision

Minimum Qualifications

• Bachelor s degree in Computer Science, Data Engineering, AI/ML, or a related technical field, equivalent practical experience.
• 5+ Years of Advanced proficiency in Python and SQL, with proven experience building, testing, and deploying production-grade data and AI solutions.
• Deep technical understanding of the Snowflake Data Cloud especially Cortex AI functions and secure data sharing.
• 2+ years of Proven experience building and deploying LLM-powered or agentic AI applications in production.
• Strong proficiency in Python and hands-on experience with LLM/agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, or the OpenAI/Anthropic SDKs).
• Experience with CI/CD practices, version control (Git), and automated testing frameworks in an enterprise environment.
• 2+ years of Practical experience with RAG, prompt engineering, and tool/function calling.
• Demonstrated experience in API integrations and systems integration (REST/GraphQL, event-driven services, authentication, enterprise data sources).
• Experience designing or implementing agent-to-agent (A2A) integrations or multi-agent communication and orchestration.
• Ability to drive technical delivery and collaborate effectively across cross-functional teams.

Preferred Qualifications

• Experience with agent interoperability standards and protocols (e.g., A2A, Model Context Protocol (MCP), OpenAPI-driven tool integration).
• Experience designing multi-agent systems, human-in-the-loop workflows, and agent guardrails for enterprise use.
• Experience with agent evaluation and observability (eval harnesses, tracing, LLMOps/AgentOps tooling such as LangSmith, Langfuse, or equivalent).
• Experience with vector databases and advanced/multimodal retrieval (e.g., hybrid search, reranking, knowledge-graph grounding).
• Experience with cloud platforms (Snowflake, AWS, GCP, or Azure) and MLOps/LLMOps practices, including cost-aware LLM routing and FinOps.
• Understanding of commerce platforms or supply chain operations.

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