Das ist der Job
Evaluate custom speech models and analyse their effectiveness against defined quality and performance benchmarks.
Darum lohnt es sich
Ability to work independently while collaborating effectively with cross‑functional teams. #J-18808-Ljbffr Responsibilities Deploy, manage, and maintain machine learning models in production environments, ensuring stable performance and operational reliability throughout the model lifecycle.
Support model lifecycle management activities, including version control, updates, monitoring, performance tracking, and continuous improvement initiatives. Conduct language benchmarking exercises across speech-to-text (STT) and text-to-speech (TTS) providers, including Azure, Deepgram, and ElevenLabs.
Compare provider performance across different languages and use cases, documenting findings and making recommendations based on evaluation results. Monitor deployed models, investigate performance issues, and contribute to ongoing optimization efforts.
Work closely with relevant stakeholders to ensure model deployments and evaluations align with business and product requirements. Background 3+ years of experience in applied machine learning and natural language processing. Strong Python programming skills and experience using Python for machine learning and model evaluation tasks.
Hands‑on experience evaluating speech-to-text and text-to-speech models, including defining evaluation criteria, running assessments, and interpreting results. Familiarity with LLM ecosystems and an understanding of how large language models are applied in real‑world solutions.
Experience deploying and managing cloud-hosted machine learning models, with Azure experience preferred. Strong analytical and problem‑solving skills, with the ability to assess model performance and communicate evaluation outcomes clearly.
