Google AI Full Stack Developer in Hartford, CT (Hybrid) :: USC & GC :: Onsite Interview

Job Title: Google AI Full Stack Engineer- Google Dialogflow CX, Google Vertex AILocation: Hartford, CT (Hybrid)- Local candidatesDuration: 6 Months (Contract)Interview: Video and Onsite Interview (2 Rounds)Visa- USC/ GC Only Job Description The LinkedIn profile must be complete, with a proper photo, and it should match the resume exactly — including title, company name, dates/tenure, and project details.Submit only local candidates and ensure they can provide valid local ID proof when required.Candidates must be available for onsite interviews whenever the client requests.No fake or mismatched profiles, please. Ensure all information is verified before submission.MUST BE LOCAL TO THE AREA - ONSITE INTERVIEW WILL BE REQUIRED FOR 2nd ROUND. I do not want any surprises when asking the candidate to come onsite for an interview that they are travelling OR not in CT this week, etc.**Hybrid 3 days a weekStart Date: As soon as possiblePosition SummaryThe Aetna IT Delivery organization, part of Client Health’s Data, Digital, Analytics, Technology (DDAT) division, is seeking a Conversational AI Engineer specializing in Google Conversational AI technologies and Retrieval Augmented Generation (RAG). You will drive the end-to-end user experience for a next-generation Conversational AI platform designed to support members and providers by accessing timely, accurate health insurance answers across digital channels.In this role, you will architect, implement, and optimize rich conversational flows using Google’s Conversational AI stack, while integrating enterprise data sources to deliver context-aware, trustworthy responses. You’ll collaborate with product, UI/UX, and backend teams to define requirements, influence features, and ensure scalable, user-centric solutions. Key ResponsibilitiesDesign and Develop Advanced Google AI Conversations Build and maintain dialog flows using Google Dialogflow CX, Google Vertex AI, or similar Google conversational platforms, with strong focus on contextual intent fulfilment.Implement Retrieval Augmented Generation (RAG) Integrate external knowledge bases and enterprise APIs with AI models to enable dynamic, contextually relevant, and reference-backed answers using RAG approaches.Apply Best Practices in Conversational AI Employ robust coding standards, NLP best practices, and reusable design patterns to ensure consistent and scalable solutions.Collaborate Across Functions Work closely with UI/UX designers, backend teams, and data engineers to define and implement APIs, data retrieval pipelines, and conversational triggers.End-to-End Chatbot Lifecycle Lead the design, development, testing, deployment, and ongoing maintenance of conversational agents, optimizing for user engagement and satisfaction.Integrate with Multiple Platforms Ensure seamless interactions across digital channels (web, mobile, voice), integrating with Google Cloud and enterprise APIs as required.Enhance Conversational Intelligence Use Google’s advanced NLP, speech-to-text, and machine learning capabilities, and Large Language Models (LLMs) to refine accuracy, dialog flow, and natural language understanding.Continuous Learning and Innovation Stay abreast of emerging generative AI, conversational technologies, and industry best practices, supporting business development through technology-driven innovation.Speech Technologies Tune and enhance speech recognition and TTS models leveraging Google Cloud Speech-to-Text and related technologies, including advanced tagging (e.g., SSML).Required Qualifications:3 years software engineering experience delivering AI-enabled applications2 years hands-on chatbot development with Google Dialogflow CX, Vertex AI, or equivalent platforms (such as Amazon Lex, LivePerson, Kore.ai, IBM Watson)2 years applying NLP and training data best practices in production conversational AI settings2 years of experience with enterprise-scale AI/chatbot frameworks, including integration with backend data and API services1 years of experience with retrieval-augmented generation techniques (RAG) or integrating external knowledge sources into LLM-driven chatbots1 years of experience in CICD, Git, unit testing, and source code management workflows1 years familiarity with cloud development/deployment principles, ideally Google Cloud Platform (GCP)1 years working in Agile environments, with practical knowledge of DevOps principles and end-to-end software lifecycle Preferred Qualifications:Backend (server-side) development experience in Java, Node.js, or PythonMulti-language proficiency, with experience in JavaScript/TypescriptPrior work with Google Cloud Speech-to-Text, Vertex AI Search, or similar voice and information retrieval technologiesFamiliarity with SSML (Speech Synthesis Markup Language) tagging for TTS refinementExperience integrating unstructured data sources (e.g., document searching using semantic embeddings) with conversational agents