Machine Learning Architect
Tiger Analytics Inc.1 year ago
Remote, United StatesStaff+
Responsibilities
- Design system architectures for ML and AI-driven solutions across multiple business verticals.
- Lead ML system design discussions and make high-level decisions for model serving, data pipelines, and MLOps frameworks.
- Architect scalable and secure cloud-native platforms for ML training, validation, deployment, and monitoring across AWS, GCP, and Azure.
- Build reusable ML lifecycle components and reference architectures.
- Define and enforce practices for model versioning, testing, CI/CD for ML, and rollback strategies.
- Deploy and manage machine learning and data pipelines in production.
- Develop containerization and orchestration solutions for model deployment.
- Collaborate with data scientists, software engineers, data engineers, product teams, business stakeholders, and global teams.
- Drive MLOps practices including monitoring, alerting, version control, and automated model deployment.
- Communicate technical problem context and business value while helping shape current and future strategy.
Requirements
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
- Typically 10+ years of hands-on experience developing and applying advanced analytics solutions in a corporate environment.
- At least 4 years of programming experience with Python.
- At least 7 years of experience productionizing, monitoring, and maintaining models.
- Strong programming skills in Python and ML libraries such as scikit-learn, TensorFlow, and PyTorch.
- Deep experience with MLOps tools including MLflow, Kubeflow, Airflow, SageMaker, or Vertex AI.
- Hands-on experience designing ML systems using AWS, Azure, or GCP.
- Strong understanding of data engineering, APIs, CI/CD pipelines, and model observability.
- Excellent communication, teamwork, and stakeholder management skills.
- Experience designing and delivering end-to-end ML solutions, scalable system architectures, model lifecycle management, and production deployment pipelines.
Benefits
- Remote, full-time position with an opportunity for significant career development in a fast-growing, challenging entrepreneurial environment.
- High degree of individual responsibility and collaboration with a global team.
Tech Stack
Categories