Machine Learning Architect

Requirements

What you’ll do in the role

• Design and define system architecture for ML and AI-driven solutions across multiple business verticals.
• Lead ML system design discussions and make high-level design choices for model serving, data pipelines, and MLOps frameworks.
• Architect scalable and secure cloud-native platforms for ML model training, validation, deployment, and monitoring (AWS/GCP/Azure).
• Build reusable components and reference architectures for various stages of the ML lifecycle.
• Define and enforce best practices in model versioning, CI/CD for ML, testing, and rollback strategies.
• Deploy and manage machine learning & data pipelines in production environments.
• Work on containerization and orchestration solutions for model deployment.
• Participate in fast iteration cycles, adapting to evolving project requirements.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
• Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
• Ability to work with a global team, playing a key role in communicating problem context to the remote teams.
• Excellent communication and teamwork skills.

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
• Typically requires 10+ years of hands‑on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
• At least 7 years of experience product ionizing, monitoring, and maintaining models.
• Strong programming skills in Python and ML libraries (e.g., scikit-learn, Tensor Flow, PyTorch).
• Deep experience with MLOps tools such as MLflow, Kubeflow, Airflow, Sage Maker, or Vertex AI.
• Hands‑on experience designing ML systems using cloud platforms like AWS, Azure, or GCP.
• Strong understanding of data engineering, APIs, CI/CD pipelines, and model observability.
• Excellent communication and stakeholder management skills.

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.