Generative-AI Engineer (Agentic AI)

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 & Development Design and implement agentic AI architectures, including:ReAct (Reason +Act)PlanandExecuteToolusing agentsMultiagent systems and coordinatorsBuild LLMpowered applications using modern foundation modelsTranslate business use cases into reliable, scalable agent workflows with clear handoffs and fallbacks LLM & RAG Engineering Develop 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 databases Tooling, APIs & Integration Build 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 & Operations Deploy 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 cost 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 & Development Design and implement agentic AI architectures, including:ReAct (Reason +Act)PlanandExecuteToolusing agentsMultiagent systems and coordinatorsBuild LLMpowered applications using modern foundation modelsTranslate business use cases into reliable, scalable agent workflows with clear handoffs and fallbacks LLM & RAG Engineering Develop 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 databases Tooling, APIs & Integration Build 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 & Operations Deploy 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 cost