- Proficiency in Python, and experience with backend systems, backend frameworks (e.g., FastAPI, Flask, Django), REST/gRPC API design, and microservices architecture.
- Experience designing and maintaining scalable, low-latency backend systems for ML or voice applications.
- Strong knowledge of system design principles, asynchronous / event-driven architectures, queuing systems, and cloud infrastructure (AWS, GCP, Azure).
- Solid grasp of testing methodologies, monitoring practices, and deployment reliability (CI/CD, infrastructure as code).
- Team player with excellent communication skills and a product-oriented mindset.
Nice-to-Have (Preferred):
- Exposure to voice or LLM-enabled systems, including speech-to-text (STT), text-to-speech (TTS), voice-streaming protocols (e.g., WebRTC), or media services like LiveKit or Twilio
- Familiarity with LLM integrations or voice‑driven pipelines, especially ML service orchestration and inference flows
- Experience with containerization, Kubernetes, or infrastructure tools (Terraform, Pulumi).
- ML pipeline integration and collaboration with ML engineers for production deployment and observability.