AI Engineer / Developer (Cloud & Communications)

Overview

Build, deploy and optimize enterprise-grade AI solutions for customer-facing Cloud and push-to-talk products and for internal technical operations, taking models from proof-of-concept to production with low latency and high reliability at the edge and in the cloud.

What you'll do

• Develop, fine-tune and deploy machine learning models (e.g., audio processing, NLP and GenAI) into production environments.
• Implement real-time noise suppression, acoustic anomaly detection and speech-to-text (ASR) pipelines optimized for high-noise environments.
• Develop backend logic and prompt engineering for cloud dispatch features, including automated radio chatter summarization and intelligent keyword alerting.
• Optimize models for resource-constrained, ruggedized Push-to-Talk devices using frameworks such as ONNX, TensorRT or TensorFlow Lite to balance latency, battery life and accuracy.
• Integrate and manage AI workloads across major cloud providers (AWS, Azure or GCP), writing scalable APIs and microservices to support AI features.
• Build and maintain secure Retrieval-Augmented Generation (RAG) pipelines to power internal knowledge assistants for IT admins and support dispatchers.
• Design and maintain CI/CD pipelines for machine learning models, automating training, testing and deployment.
• Implement real-time monitoring of model performance, data drift and latency, proactively mitigating degradation in zero-fault tolerance communication environments.
• Leverage AI APIs to build internal scripts and tools for code reviews, automated QA test generation and predictive cloud infrastructure maintenance.

What you'll need

• 3+ years of hands-on experience as a Machine Learning Engineer, AI Developer or Software Engineer with a strong focus on applied AI, ideally within telecommunications, UCaaS or cloud SaaS.
• Commitment to writing clean, scalable and maintainable production code.
• Experience deploying software in high-availability, mission-critical environments.
• Ability to translate a high-level strategic requirement into a concrete, functioning software solution.
• Expert-level proficiency in Python.
• Deep experience with PyTorch, TensorFlow or JAX.
• Practical experience implementing DSP concepts, speech recognition (ASR) and audio-based neural networks.
• Hands-on experience with Docker, Kubernetes and cloud ML services (e.g., AWS SageMaker, Azure ML, Vertex AI).
• Proven ability to quantize, prune and optimize models for mobile or edge hardware deployments.

Nice to have

• Strong working knowledge of C++ (for edge deployment and audio processing).
• Familiarity with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Hugging Face).

Details

• Location: Bangalore, India.
• Travel: Under 10%.
• Relocation: Not provided.
• Position type: Experienced.
• Referral payment plan: No.