AI Solutions Architect

AI Solutions Architect Work Location: Hybrid (Chicago, IL or Houston, TX) Job Type: W2 Contract Only Position Summary We are seeking an experienced AI Solutions Architect to lead the design, development, and deployment of next-generation AI and Generative AI solutions within enterprise Contact Center environments. This role will be responsible for architecting intelligent customer experience solutions that leverage Large Language Models (LLMs), Agentic AI, Conversational AI, Retrieval-Augmented Generation (RAG), predictive analytics, and cloud-native technologies to improve customer satisfaction, agent productivity, and operational efficiency. The ideal candidate combines deep AI/ML expertise with hands-on experience supporting Contact Center platforms, customer service operations, conversational AI, workforce optimization, and customer experience transformation initiatives. Key Responsibilities Architect and deliver end-to-end AI and Generative AI solutions supporting Contact Center and Customer Experience (CX) organizations. Design and implement intelligent virtual agents, AI-powered agent assist capabilities, knowledge management solutions, and automated customer interaction workflows. Develop RAG-based applications, conversational AI platforms, multi-agent systems, and LLM-powered customer support solutions. Partner with Contact Center leaders, operations teams, and business stakeholders to identify opportunities for AI-driven automation and process optimization. Create AI solutions to improve call deflection, first-call resolution, customer sentiment analysis, agent productivity, quality monitoring, and root cause analysis. Design scalable cloud architectures supporting high-volume customer interactions across voice, chat, email, and digital channels. Lead development of predictive analytics models for customer behavior, churn prediction, workforce planning, and operational forecasting. Establish AI governance, MLOps frameworks, model monitoring, and responsible AI best practices. Collaborate with engineering teams to develop streaming and batch data pipelines supporting real-time customer engagement use cases. Present solution architectures, business cases, and AI transformation strategies to executive and senior leadership teams. Create reference architectures, technical standards, and implementation roadmaps for enterprise AI adoption. Provide technical leadership and mentorship to data scientists, engineers, architects, and business teams. Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical discipline. 10+ years of experience in AI/ML, Solution Architecture, Data Analytics, Cloud Architecture, or Customer Experience technologies. Proven experience architecting and deploying enterprise AI and Generative AI solutions. Experience supporting Contact Center, Customer Experience, Customer Service, or Customer Operations organizations. Strong understanding of customer service workflows, knowledge management, conversational AI, and customer engagement strategies. Experience designing solutions utilizing LLMs, RAG architectures, Agentic AI, and conversational AI technologies. Hands-on experience with machine learning frameworks and cloud AI services. Experience working with executive stakeholders and driving large-scale digital transformation initiatives. Strong communication and stakeholder management skills. Preferred Qualifications Experience with Contact Center platforms such as Genesys, NICE CXone, Five9, Amazon Connect, Cisco Contact Center, or similar technologies. Google Cloud Professional Machine Learning Engineer certification or equivalent cloud certification. Experience implementing AI-powered agent assist, quality monitoring, call summarization, or customer analytics solutions. MBA or advanced degree focused on Business Analytics, Data Science, Innovation, or Technology Management. Experience within telecommunications, customer service, customer operations, or enterprise technology environments. Technical Skills AI & Generative AI Generative AI. Large Language Models (LLMs). Retrieval-Augmented Generation (RAG). Agentic AI. Multi-Agent Systems. Prompt Engineering. Conversational AI. Intelligent Virtual Agents. LangChain. Vertex AI. TensorFlow. PyTorch. AutoML. MLOps. Contact Center & Customer Experience AI Agent Assist. Contact Center Automation. Customer Sentiment Analysis. Call Summarization. Knowledge Management. Customer Journey Analytics. Workforce Optimization. Conversational Analytics. Quality Monitoring. Customer Self-Service Solutions. Data & Analytics Apache Spark. SQL. BigQuery ML (BqML). Tableau. Power BI. Looker. Alteryx. Predictive Analytics. Real-Time Data Processing. Cloud & Development Google Cloud Platform (GCP). Python. SQL. R. Docker. CI/CD Pipelines. API Integrations. Data Engineering. Desired Outcomes Improve customer experience through AI-driven automation and intelligent customer interactions. Reduce average handle time and improve first-call resolution metrics. Increase agent productivity through AI-powered assistive technologies. Deliver scalable Generative AI solutions that drive measurable business value and ROI. Establish enterprise-wide AI capabilities supporting customer engagement, service operations, and digital transformation initiatives. E-Solutions