Agentic AI Engineer

About Advanced Energy Advanced Energy (Nasdaq: AEIS) is a global leader in the design and manufacturing of highly engineered, precision power conversion, measurement and control solutions for mission-critical applications and processes. AEs power solutions enable customer innovation in complex applications for a wide range of industries including semiconductor equipment, industrial, manufacturing, telecommunications, data center computing and healthcare. With engineering know-how and responsive service and support around the globe, the company builds collaborative partnerships to meet technological advances, propel growth for its customers and innovate the future of power. Advanced Energy has devoted four decades to perfecting power for its global customers and is headquartered in Denver, Colorado. Role Summary The Agentic AI Engineer is responsible for designing, building, and deploying intelligent, AI agents capable of reasoning, planning, and executing complex tasks across business and technical domains. This role focuses on productiongrade agentic systems powered by Large Language Models (LLMs), integrated with enterprise tools, APIs, and data sources. This is a hands-on role requiring endtoend ownership from agent architecture and development through deployment, monitoring, evaluation, and continuous optimization. The ideal candidate combines strong software engineering fundamentals with deep expertise in LLM application design, agent frameworks, RetrievalAugmented Generation (RAG), memory systems, and multiagent orchestration. In addition to strong engineering skills, this role requires a product mindsetpartnering with business stakeholders to shape use cases, select appropriate agentic tools and frameworks, and translate user needs into scalable technical solutions. Key Responsibilities Agent Architecture & DevelopmentDesign and implement agentic AI architectures, including: ReAct (Reason + Act)PlanandExecuteToolusing agentsMultiagent systems and coordinatorsBuild LLMpowered applications using modern foundation models Translate business use cases into reliable, scalable agent workflows with clear handoffs and fallbacksLLM & RAG EngineeringDevelop and optimize prompt engineering strategies for reasoning, task execution, and tool use.Implement RetrievalAugmented Generation (RAG) pipelines using structured and unstructured data.Design and manage agent memory systems, including:Shortterm (conversation/state)Longterm (vectorbased memory)Work with vector databasesTooling, APIs & IntegrationBuild and integrate internal and external tools (APIs, microservices, enterprise systems).Enable agents to safely interact with: DatabasesBusiness applicationsAutomation platformsCloud servicesEnsure robust error handling, retries, and fallback logic.Production Readiness & OperationsDeploy agentic systems into production environments.Implement monitoring, logging, and evaluation frameworks for: Accuracy and task successLatency and cost efficiencySafety and policy complianceDesign and enforce guardrails to prevent hallucinations, unsafe actions, or data leakage.Continuously optimize system performance and costCollaboration & DeliveryPartner closely with business stakeholders, application developers, data teams, and business stakeholders.Act as a technical product owner for agentic solutions: collect user and business requirements, clarify problem statements, and translate them into system designs and implementation plansIdentify highimpact AI opportunities and rapidly prototype, validate, and scale solutions.Contribute to internal best practices, architecture standards, and AI governance.Lead agentic tooling decisions: evaluate frameworks, model providers, orchestration patterns, and integration approaches; make pragmatic build/buy choices aligned to security, reliability, and cost constraints.Act as a technical product partner: collect user requirements, define success metrics and acceptance criteria, translate needs into solution designs and implementation plans, and drive delivery from prototype to production.Required QualificationsBachelors degree in Computer Science, Computer Engineering, Information Technology, or equivalent experience.3+ years of professional software development experience.Strong proficiency in Python (required).Handson experience working with LLM APIs, and MCPSolid understanding of: Prompt engineeringEmbeddings and semantic searchRetrievalAugmented Generation (RAG)API and backend system designExperience integrating AI systems into production applications.Familiarity with cloud platforms (Azure, AWS, or GCP).Strong problemsolving skills and ability to manage multiple priorities in fastpaced environments.Excellent written and verbal communication skills in EnglishPreferred SkillsExperience with agent frameworks (e.g., LangChain, Semantic Kernel, AutoGen, CrewAI).Knowledge of multiagent coordination patterns.Experience with evaluation frameworks for LLMs and About Advan