Overview
As a Machine Learning Engineer, you will play a critical role in developing cutting-edge conversation intelligence technology for a fast-scaling AI business. Working fully remotely, you will leverage your expertise in machine learning, MLOps, and platform engineering to transform existing models into production-ready components and build resilient ML pipelines. This position offers significant responsibility in shaping technical direction and requires strong collaboration with a growing team focused on innovation and market expansion.
Responsibilities
- Refactor and upgrade existing ML models into modular, production-ready components.
- Build resilient ML pipelines using DAG-based orchestration tools such as Argo Workflows or Airflow.
- Define and automate evaluation pipelines using golden datasets to ensure model quality.
- Design provenance tracking for models, prompts, and input data to ensure auditability.
- Implement shadow testing and A/B testing infrastructure on live traffic.
- Engineer human-in-the-loop feedback pipelines for model training.
- Integrate third-party AI APIs and develop telemetry for tracking costs and performance.
Requirements
- Proven experience across machine learning, MLOps, and platform/backend engineering.
- Strong understanding of evaluation metrics for generative AI, LLMs, and speech models.
- Hands-on experience with Docker, Kubernetes, and modern orchestration frameworks.
- Demonstrated track record building observable ML systems with monitoring and cost attribution.
- Familiarity with data provenance and compliance standards.
- Experience in developing fail-safe mechanisms for sensitive data and AI outputs.