Machine Learning Engineer

What you'll do

• Develop ML systems and pipelines spanning data ingestion, training, testing, deployment, and monitoring.
• Implement MLOps practices, including model versioning, experiment tracking, CI/CD, and production monitoring.
• Integrate and optimize models in real-time products, ensuring data quality, observability, reliability, and scalability.

What they ask for

• Strong experience in machine learning, deep learning, and NLP.
• Background in MLOps and data pipelines, including production deployment, monitoring, and scaling.
• Proficiency in Python and familiarity with Go.
• Experience with ML lifecycle management tools, such as MLflow, Kubeflow, or Weights & Biases.

Nice to have

• Experience in speech recognition, TTS, or audio processing.
• Familiarity with LLMs, generative AI, real-time inference systems, or data orchestration frameworks.

Tools

Python, Go, MLflow, Kubeflow, Weights & Biases, Airflow, Prefect, Dagster

AI focus

Develop AI models for human-like conversations, including speech, language, and intelligence, and deploy them in real-time products.