Google Cloud Platform Application Technical (Engineer)

Role: Google Cloud Platform Application Technical (Engineer)

Location : Charlotte, NC

Contract 

Job Summary

• 8+ 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 BigQuery. 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.