Movius - MLOps Engineer - CPaaS

Role Summary :

We are looking for an experienced ML Ops Engineer (38 years) with a strong background in operationalizing AI/ML systems, particularly in speech AI agents integrated into telephony environments.

This role bridges AI infrastructure, voice bot deployment, and real-time streaming operations, enabling AI models to function reliably in production use cases such as customer collections, voice-based load bookings, and conversational AI in logistics and telecom.

Key Responsibilities :

- Build, deploy, and maintain real-time speech AI agents for telephony (inbound/outbound) using CPaaS platforms and audio pipelines.

- Integrate ASR (Automatic Speech Recognition), TTS (Text-to-Speech), and LLMs with voice bot workflows using WebSocket or RTP streams.

- Work closely with backend engineers and AI scientists to deploy scalable ML models into production across Kubernetes, Docker, and cloud-native environments (AWS/GCP).

- Set up observability, logging, and monitoring of speech pipelines (latency, dropouts, stream integrity) using Prometheus, Grafana, ELK, etc.

- Automate the ML lifecycle and CI/CD for AI models using tools like MLflow, Airflow, or Kubeflow.

- Manage streaming latency optimization across ASR, LLM, and TTS chains in low-latency applications like telephony bots.

- Ensure fault-tolerant, secure, and compliant deployment of voice-based systems using industry-standard DevOps practices.

Required Skills :

- 58 years of experience in ML Ops, AI platform engineering, or DevOps with hands-on ML deployment.

- Experience with ASR/TTS model integration (e.g., Whisper, Amazon Polly, Google Speech API).

- Hands-on experience deploying AI in telephony environments, with working knowledge of Asterisk, FreeSWITCH, or similar systems.

- Solid command over WebSocket, SIP, RTP, or similar streaming protocols for voice integration.

- Proficient with cloud services (AWS/GCP/Azure), containers (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform).

- Experience with end-to-end CI/CD pipelines, GitOps, and model monitoring (accuracy, drift, performance).

- Strong programming in Python and scripting to support infrastructure and integration tasks.

Nice to Have :

- Exposure to CPaaS platforms (e.g., Twilio, Vonage, Kaleyra, Exotel) for voice bot integration.

- Familiarity with conversational AI/NLU platforms and agentic AI frameworks.

- Prior experience supporting AI agents in collections, logistics/freight, or telecom workflows.

What We Offer :

- Work on next-gen AI voice agents deployed in high-impact customer environments.

- Influence platform architecture and deliver cutting-edge infrastructure for real-time ML.

- High-ownership, engineering-led culture with strong product alignment and technical mentorship.