Agentic AI Solutions Architect, Conversational AI

What You Will Do: Architect and build AI agents that understand intent, keep context across a conversation, and complete multistep tasks for real customers Design end to end cloud solutions that connect large language models to contact center platforms such as Amazon Connect and Amazon Lex Build the APIs, microservices, and integrations that let agents look up records and take action in enterprise systems Design agent orchestration, tool calling, retrieval augmented generation (RAG), and prompt strategies, then measure and improve accuracy, latency, and containment Build in security, privacy safeguards, and clean escalation to a live agent Move fast with a small, high performing team: prototype, ship, measure, and iterate through CI/CD Required Bachelor’s degree in Computer Science, Engineering, or a related field and 12+ years of relevant experience (additional experience may be considered in lieu of a degree) Solutions architecture experience: you have designed and built services that run in production on at least one major cloud (AWS, Azure, or Google Cloud) Hands on development in Python, JavaScript/Node.js, Java, or a similar language Experience building or integrating LLM powered systems, conversational AI, or AI/ML solutions Working knowledge of agentic AI: orchestration, tool calling, context management, and automated workflows REST APIs, microservices, and enterprise integration Flexible work location: remote, hybrid, or onsite in the Baltimore, MD area (remote team members travel to Baltimore about once a month) Eastern time zone preferred; remote candidates should be able to work East Coast business hours Ability to obtain and maintain a Public Trust Preferred Professional level cloud certification: AWS Certified Solutions Architect Professional, Microsoft Azure Solutions Architect Expert, or Google Cloud Professional Cloud Architect Amazon Connect experience of any depth, or contact center work with Amazon Lex, Genesys Cloud, or Twilio LangChain, LangGraph, Semantic Kernel, Bedrock Agents, or similar frameworks AWS Bedrock, Azure OpenAI, or OpenAI APIs Voice AI with speech to text and text to speech RAG, vector databases, and semantic search Serverless, containers, and Kubernetes (Lambda, API Gateway, Docker) LLM evaluation, guardrails, and responsible AI practices