Staff Machine Learning Engineer - VoIP Infrastructure

As a Staff Machine Learning Engineer - VoIP Infrastructure you will: 

• Contribute to the design, development and implementation of VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads  
• Collaborate with engineering, Product, and infrastructure teams to ensure our voice and AI platforms perform efficiently, scale reliably, and integrate seamlessly across SIP/RTP, Kamailio, RTPEngine, and related telecom systems.  
• Contribute to the continuous improvement of the SRE practice by turning operational telephony and AI workload use cases into requirements for software tooling.  
• Contribute to the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments.  
• Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.).  
• Work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery — spanning both telephony infrastructure and LLM integration layers.  
• Experience integrating LLMs into voice platforms and real-time communication systems.  
• Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.  

To be successful in this role you have: 

• Hands-on experience building VoIP systems using SIP/RTP protocols;    
• Practical knowledge of Kamailio, RTPEngine, FreeSWITCH, SBCs, and PSTN systems (or similar);  
• Working knowledge of PSTN infrastructure and telecom protocols;  
• Experience integrating applications on top of LLMs (using existing models, not building them);  
• Experience in prompt engineering and developing LLM based features   
• 4+ years of development experience with Python, GoLang, Java or similar languages.  
• 4+ years of experience operating highly available distributed workloads on Kubernetes following a DevOps approach.   
• Working experience building distributed systems with cloud-native software;   
• Experience with software-defined networking, infrastructure as code and configuration management;  
• Experience with DevOps tooling  (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset  
• Experience building software for compliance and security in regulated environments is considered an asset  
• 4+ years of experience with infrastructure and platform operations, deployments, SRE, and DevOps with a continued focus on improving Platform health is considered an asset