ML Ops Engineer – Speech AI

2 days ago

Bengaluru, Karnataka, India Movius Full time

Hi All,

We are hiring Please find below the Job Description (JD) for the ML Ops Engineer role we are currently looking to fill. This position will play a critical role across both our AI Platform and Speech AI delivery and observability efforts .

Location: Bangalore (India)

Full-Time | Immediate Joiners Preferred

Role Summary:

We are looking for an experienced ML Ops Engineer (3–8 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:

  • 5–8 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.

Pls share below details with updated CV to if the JD suits your profile:

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Regards,

Lavanya

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