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

Employment type
Full-time

Work arrangement
Yes