Google Cloud Platform AI Engineer – CCaaS (Contact Center), Gemini (GECX)
Why This Role Stands Out
Leverage your expertise in Google Cloud Platform and AI to build cutting-edge Contact Center as a Service solutions, driving innovation in customer experience. This hybrid role is perfect for experienced AI engineers who thrive on complex challenges and want to shape the future of conversational AI. Apply today to join a forward-thinking team and make a significant impact.
Quick Overview
Job Description
Google Cloud Platform AI Engineer – CCaaS (Contact Center), Gemini (GECX)
Location : Charlotte, NC / Sunrise, FL
- 10+ years of commercial software development experience.
- Design and implement scalable CCaaS and IVA solutions leveraging leading cloud and enterprise conversational AI/customer service platforms, including conversational IVR design, NLU/NLP modeling, intent and flow orchestration, webhook integrations, and speech-to-text/text-to-speech capabilities.
- Develop secure and resilient cloud infrastructure on Google Cloud Platform using services such as GKE, Cloud Run, Cloud Functions, Pub/Sub, Apigee, and BigǪuery. Experience with IAM, VPC design, encryption, multi-region high availability, and Infrastructure as Code (Terraform) to support enterprise-grade customer experience platforms.
Key Responsibilities
- Implement and optimize CCaaS solutions, including ACD (Automatic Call Distribution), skills-based routing, dialers, omnichannel capabilities, and campaign management while ensuring scalable, secure, and compliant contact center operations.
- Lead integrations and migrations leveraging CCaaS APIs and telephony capabilities, including CRM/CTI integrations, webhooks, SIP/WebRTC, security configurations, and migration from legacy contact center platforms to cloud-based solutions.
- Design, develop, and maintain data pipelines, models, and datasets supporting CCaaS data platforms while ensuring data quality, reliability, security, and compliance.
Skill Requirements
- Experience building and integrating AI/ML solutions such as XGBoost, SARIMA, Prophet, TensorFlow, and prompt-engineering-based applications into production environments.
- Strong understanding of the ML model lifecycle, including evaluation, hyperparameter tuning, monitoring, governance, bias mitigation, and explainability.
- Experience with Agile development methodologies, CI/CD, DevOps, and observability practices.
- Hands-on experience with Kafka, relational databases, and/or NoSQL databases.
- Strong understanding of data structures, algorithms, and design patterns, with a proactive mindset toward continuous improvement.
Skills
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