BENGALURU · FULLTIME
AI Operations Engineer
Cisco Systems
Bengaluru · onsite · Posted 29d ago
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Undisclosed7–12 yrsfulltimeBengaluru
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Section · 01
About this role
Job Summary
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Meet the Team
Join Cisco s Commerce Operations Engineering Center (COEC) as an AI Engineer, where you will be part of a dynamic team driving innovation through AI-powered automation. Collaborate with multi-functional AI, engineering, and operations teams to transform sophisticated operational challenges into reliable, scalable AI solutions that advance Cisco s AI-first future.
Your Impact
As an AI Engineer in COEC, you will design, build, and operate production-ready AI agents that power Cisco s Commerce Operations. You will translate complex operational needs into automated workflows by orchestrating LLM reasoning, tool/function calling, retrieval, enterprise integrations, and agent-to-agent communication. You will be responsible for the full lifecycle of AI agent delivery-from solution design, guardrails, and system integration to evaluation, deployment, monitoring, and continuous improvement. Your work will help eliminate manual tasks, improve speed and accuracy, and drive operational excellence.
What You ll Do
- Design and build AI agents that automate manual and repetitive Commerce Operations tasks.
- Implement agent orchestration using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent.
- Build agent-to-agent integrations using emerging standards such as A2A and MCP to enable reliable coordination and delegation.
- Design secure API integrations with commerce platforms and enterprise data sources using REST, GraphQL, webhooks, event-driven services, authentication, rate limiting, and error handling.
- Build and optimize retrieval-augmented generation (RAG) pipelines using documents, structured data, and knowledge repositories.
- Implement guardrails, human approval gates, LLM routing, and failover mechanisms to ensure reliability, safety, and cost control.
- prompt and context engineering to ensure AI agents deliver accurate, safe, and consistent outcomes.
- Build evaluation and observability capabilities, including offline test sets, online monitoring, tracing, audit logging, and quality regression detection.
- Own deployment and production operations, including CI/CD, progressive rollout, rollback, monitoring, model/version control, and cost tracking.
- Supervise AI solution performance and report business impact through critical metrics such as efficiency gains, cycle-time reduction, incident reduction, and operational accuracy.
- Continuously improve AI workflows, tools, and methodologies to increase reliability, scalability, and business value.
- Partner with Commerce Operations, application development, infrastructure, and business teams to integrate AI solutions into existing systems and workflows.
Minimum Qualifications
- 7+ years of experience in software engineering or a related field within an enterprise environment.
- Shown experience building and deploying LLM-powered or agentic AI applications in production.
- Strong Python skills and tactical experience with LLM/agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, OpenAI SDK, Anthropic SDK, or equivalents.
- Practical expertise in retrieval-augmented generation (RAG), prompt engineering, tool/function calling, agent orchestration, and multi-agent communication or workflow orchestration.
- Experience with API and system integrations, including REST, GraphQL, event-driven services, authentication, and enterprise data sources.
- Demonstrated ability to lead technical delivery and collaborate effectively with multi-functional business and engineering teams.
Preferred Qualifications
- Experience with agent interoperability standards and protocols, including A2A, Model Context Protocol (MCP), and OpenAPI-based tool integration.
- Skilled in designing enterprise-grade multi-agent systems, human-in-the-loop workflows, and implementing agent guardrails.
- Validated experience with agent evaluation and observability tools such as eval harnesses, tracing, LangSmith, Langfuse, or equivalent AgentOps/LLMOps platforms.
- Knowledge of vector databases and sophisticated retrieval methods, including hybrid search, reranking, multimodal retrieval, and knowledge-graph grounding.
- Familiarity with AI governance and safety practices, including RBAC, audit logging, PII handling, and controls for high-risk automated actions.
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps/LLMOps practices, including cost-aware LLM routing and FinOps.
- Understanding of commerce platforms, commerce operations, or supply chain operations.
- Consistent record of leading end-to-end AI or agentic AI delivery in sophisticated enterprise environments.
- Advanced degree in AI, Machine Learning, Computer Science, or related field preferred.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Section · 02
Skills
GraphQLAutomationCrewaicloud platformslaw enforcementgoogle cloud platformamazon web servicesFinOpsai engineerintegrationsai governanceBankingLarge Language Models (LLMs)Error HandlingdeploymentcloudCiscoApplication DevelopmentAi SolutionsMicrosoft AzureLangchainArtificial IntelligenceAnthropic SDK
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About Cisco
Cisco Systems
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