Machine Learning Engineer

## About the Role DataQueue is the largest and fastest-growing Voice AI company in the MENA region, deploying AI voice agents across banks, telecoms, and governments — and scaling rapidly. VoiceHub, our platform, enables businesses to design, test, and deploy AI voice agents at scale, combining LLMs with a full voice stack including TTS, STT, copilots, speech analytics, and real-time workflows across 25+ languages. We are looking for a Machine Learning Engineer with strong expertise in Large Language Models (LLMs), Speech-to-Text (STT), and Text-to-Speech (TTS) to help us build and optimize the next generation of voice AI systems. In this role, you will work on real-world production problems across model development, inference optimization, deployment, and scale. You will be part of a team building intelligent conversational systems that must perform reliably under real enterprise requirements, with high standards for latency, accuracy, scalability, and natural interaction quality. ## Responsibilities - Model Development: Research, develop, fine-tune, and improve LLM, STT, and TTS models for real-world conversational AI applications. - Training & Inference Pipelines: Build and maintain efficient training, inference, and deployment pipelines for machine learning models in production environments. - Optimization: Optimize models and pipelines for latency, accuracy, throughput, cost efficiency, and scalability. - Data Work: Work with large-scale text and audio datasets, including preprocessing, augmentation, curation, and evaluation. - Production Integration: Collaborate closely with software and platform engineers to integrate ML models into production-grade systems. - Inference Performance: Improve real-time inference performance using modern frameworks and deployment techniques suited for large-scale AI systems. - Research & Experimentation: Stay up to date with advancements in LLMs, speech technologies, deep learning, and model optimization, and apply them where relevant. - Engineering Standards: Participate in code reviews and contribute to improving internal ML development workflows, tooling, and best practices. ## Requirements - Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field - 3+ years of experience in machine learning and deep learning - Strong proficiency in Python - Strong hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX - Experience working with LLMs such as GPT, BERT, or similar transformer-based models - Experience working with STT models such as Whisper or similar - Experience working with TTS models such as Tacotron, VITS, or similar - Strong understanding of NLP concepts including tokenization, embeddings, transformers, and sequence modeling - Experience building or optimizing training and inference pipelines - Familiarity with cloud-based ML environments such as AWS, GCP, or Azure - Experience optimizing models for low-latency or production inference - Strong understanding of data preprocessing, augmentation, and evaluation methodologies - Experience with vector databases and retrieval-augmented generation (RAG) - Familiarity with high-performance inference frameworks such as vLLM and TensorRT-LLM - Familiarity with model distillation and quantization techniques ## Strong Signal We would be especially interested in candidates who have already: - Shipped ML models into production, not just trained them in research settings - Worked on real-time or low-latency AI systems - Optimized inference performance for LLM, STT, or TTS workloads - Built systems that combine multiple AI components in one production pipeline - Worked with large-scale audio and text datasets in practical environments - Balanced research thinking with strong engineering execution - Experience with multilingual speech or language systems ## Nice-to-Have Skills - Experience with MLOps practices, including CI/CD for ML systems - Familiarity with model serving and deployment infrastructure - Understanding of privacy, security, and compliance considerations in AI applications - Familiarity with conversational AI or voice agent platforms ## Why Join DataQueue - Work with one of the fastest-growing AI companies in the MENA region - Build systems that solve real problems for enterprises at scale - Join our vision to become the voice infrastructure of the region - Work on production systems across LLMs, STT, TTS, and real-time voice workflows - Be part of a team that moves fast, builds seriously, and operates with high standards ## This Role Is Not For You If - Your experience is mostly academic without strong engineering execution - You have only experimented with models but not worked on production systems - You are looking for a role focused only on theory or research without deployment responsibility