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. Basic Qualification- 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 productionizing, monitoring, and maintaining models. Strong programming skills in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch). Deep experience with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, 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. Benefits 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. #J-18808-Ljbffr Tiger Analytics Inc.