Technical Project Manager

Role & responsibilities

Voice AI Development

• Design and build real-time voice-based AI agents capable of natural, low-latency conversations.
• Develop and integrate Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Natural Language Understanding (NLU) pipelines entirely or predominantly in-house.
• Implement wake-word detection, speaker diarization, noise cancellation, and voice activity detection (VAD) modules.
• Build dialogue management systems with context tracking, intent handling, and multi-turn conversation flows.
• Optimize ASR/TTS models for accuracy, latency, and domain-specific vocabulary.

Infrastructure & Platform Engineering

• Design and manage the cloud or on-premise infrastructure required to deploy, scale, and monitor voice AI workloads.
• Set up CI/CD pipelines, containerization (Docker/Kubernetes), and model serving infrastructure (e.g., Triton, TorchServe, custom gRPC services).
• Ensure high availability, fault tolerance, and low-latency performance of real-time voice systems.
• Implement logging, monitoring, and alerting for production AI systems.
• Reduce dependency on external vendors by building reusable, internal libraries and services.

In-House Solution Design

• Evaluate open-source ASR (e.g., Whisper, Wav2Vec2, Kaldi), TTS (e.g., Coqui, VITS, FastSpeech2), and LLM frameworks and adapt them for production use.
• Build custom model training and fine-tuning pipelines for domain-specific voice applications.
• Contribute to internal tooling, SDKs, and APIs that allow other teams to build voice-powered features.

Collaboration & Leadership

• Collaborate with product, design, and data teams to translate business requirements into technical solutions.
• Conduct code reviews, mentor junior engineers, and enforce engineering best practices.
• Document architecture decisions, APIs, and operational runbooks.

Required Qualifications

Experience

• 5 years of total software development experience.
• Minimum 1 year of hands-on experience building and deploying AI agents in a production environment.
• Proven track record of building in-house AI / ML solutions with limited dependency on external SaaS platforms.

Technical Skills Voice AI

• Strong experience with ASR systems: Whisper, Wav2Vec2, DeepSpeech, Kaldi, or equivalent.
• Experience with TTS engines: Coqui TTS, VITS, FastSpeech2, Mozilla TTS, or equivalent.
• Hands-on experience with NLU/dialogue frameworks: Rasa, custom transformer-based models, or similar.
• Familiarity with audio processing libraries: librosa, PyDub, SpeechBrain, WebRTC VAD.
• Understanding of real-time audio streaming protocols (WebSockets, WebRTC, SIP/VoIP).

Technical Skills – Infrastructure

• Proficiency with Python and worked around 1 year in PHP-Larvel.
• Experience with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure).
• Familiarity with ML model serving and optimization (ONNX, TensorRT, Triton Inference Server).
• Experience with message brokers and streaming platforms (Kafka, RabbitMQ, or Redis Streams).
• Working knowledge of databases: PostgreSQL, Redis, and vector databases (Pinecone, Weaviate, or Qdrant).

Good to Have

• Experience with on-device / edge AI for voice (TFLite, CoreML, ONNX Runtime).
• Familiarity with telephony stacks and VoIP platforms (Asterisk, FreeSWITCH, Twilio Programmable Voice).
• Experience fine-tuning LLMs (LLaMA, Mistral, Falcon) for conversational use cases.
• Contributions to open-source voice AI or ML projects.

What We Offer

• Opportunity to build a cutting-edge voice AI platform from the ground up.
• High ownership, low bureaucracy — your decisions directly shape the product.
• Competitive compensation and performance-linked incentives.
• Flexible working arrangements.
• Access to GPU compute resources and a dedicated AI research budget.