ML Engineer

Requirements

• Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecycle.
• Architect and deliver AI-powered solutions enabling natural speech interaction and real-time audio understanding.
• Develop and optimize ML models focused on audio data to extract business-critical insights from previously unstructured voice data.
• Collaborate with cross-functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications.
• Work directly with customers to identify needs, gather feedback, and deliver impactful real-world solutions.
• Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments.
• Participate in continuous improvement of the ML infrastructure and processes for scalability and performance.
• Proven experience building and deploying ML models into production environments.
• Demonstrated ability to own the full model lifecyclefrom data ingestion and model development to deployment and monitoring.
• Experience with audio-focused ML projects or similar domains involving unstructured data.
• Proficiency in building scalable data pipelines for model training and evaluation.
• Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.