MLOPS Engineer

Overview
Tachyon Cortex Machine Learning AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions.

Key Responsibilities

• Develop and maintain ML pipelines using tools like MLflow Kubeflow or Vertex AI.
• Automate model training testing deployment and monitoring in cloud environments (e.g. GCP AWS Azure).
• Implement CI/CD workflows for model lifecycle management including versioning monitoring and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM documentation explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
• LeverageAutoML tools(e.g. Vertex AI AutoML H2O Driverless AI) for low-code/no-code model development documentation automation and rapid deployment

Qualifications

• 10 Years of professional experience in Software Engineering & 3 Years in AIML Machine Learning Model Operations.
• Strong proficiency in Java and Python SQL and ML libraries (e.g. scikit-learn XGBoost TensorFlow PyTorch).
• Experience with cloud platforms and containerization (Docker Kubernetes).
• Familiarity with data engineering tools (e.g. Airflow Spark) and ML Ops frameworks.
• Solid understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.