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About the Role:
- Bridge the gap between applied machine learning and robust platform engineering.
- Translate AI capabilities into reliable and observable system components.
- Ensure ML models meet strict quality, cost, and latency thresholds before production.
Key Responsibilities:
- Refactor and upgrade ML models, including NLP, generative AI, and transcription, into production-ready modular contracts.
- Define and automate strict evaluation pipelines using golden datasets.
- Implement tracking for model, prompt, and input data provenance, plus telemetry for compute cost and latency.
Requirements:
- Proven track record at the intersection of Machine Learning, MLOps, and Platform/Backend Engineering.
- Deep understanding of evaluation metrics for generative AI, LLMs, and speech models.
- Strong hands-on experience with Docker, Kubernetes, and modern orchestration frameworks.
Smart Working
Smart Working connects skilled professionals with outstanding global teams and products for full-time, long-term roles. It is a remote-first company known as one of the highest-rated workplaces on Glassdoor, fostering a genuine community that values growth and well-being.