Technical Project Manager – Devices & Release Lead
Listed on 2026-10-23
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IT/Tech
Cloud Computing: Infrastructure & Operations, AI Engineer (Applied/Software), Data Engineering
Skill: GCP; CI/CD;
Maven;
Jenkins;
Git (Git Hub, Git Lab, Bit Bucket, SVN); SQL; RDBMS;
Java;
Kafka
Must have Tech:
Experience 4+ Years
Skills:
CCaaS/CTI/CRM, IVA, IVR, Java, Microservices, Springboot, Kafka, AI/ML(Sarima/Prophet), Release
Management (
Maven/Git)
Good to have
Tech:
Good knowledge with Understanding of model lifecycle, Good understanding of data structures, algorithms and design patterns
1. 4+ years of commercial software development experience.
2. Design and implement scalable CCaaS and IVA solutions leveraging leading cloud and enterprise conversational AI/customer service solutions, including conversational IVR design, NLU/NLP modeling, intent and flow orchestration, webhook integrations, and speech-to-text/text-to-speech.
3. Develop secure, resilient cloud infrastructure on major cloud service providers using services such as GKE, Cloud Run, Cloud Functions, Pub/Sub, Apigee, and Big Query, implementing IAM, VPC design, encryption, multi-region high availability, and Infrastructure as Code (Terraform) to support enterprise-grade customer experience platforms.
4. Implement and optimize CCaaS solutions, including ACD (Automatic Call Distribution), skills-based routing, dialer, omnichannel capabilities, and campaign management, ensuring scalable, secure, and compliant contact center operations.
5. Experience in integrations and migrations leveraging CCaaS APIs and telephony capabilities, including CRM/CTI integrations, webhooks, SIP/WebRTC, security configuration, and transition from legacy contact center platforms to cloud-based solutions.
6. Design, develop, and maintain data pipelines, models, and datasets to support CCaaS data platforms while ensuring data quality, reliability, security, and compliance.
7. Experience building and integrating AI/ML solutions such as XGBoost, SARIMA, Prophet, prompt engineering, and Tensor Flow into production systems.
8. Understanding of the model lifecycle, including evaluations, hyperparameter tuning, ongoing monitoring, model governance, bias mitigation, and explainability.
9.
Experience with Agile development, CI/CD, Dev Ops, and observability.
10. Hands-on experience with Kafka, relational databases, and/or No
SQL databases.
11. Understanding of data structures, algorithms, and design patterns.
12. Proactively identifies continuous improvement opportunities beyond the obvious.
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