Senior ML Architect – End-to-End AI Systems & MLOps Lead

Tiger Analytics

United States

Remote

USD 180,000 - 240,000

Full time

14 days+

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Job summary

Tiger Analytics is seeking an experienced Principal Data Scientist to lead end-to-end ML architecture and production-grade deployments for global clients. You will drive scalable data platforms, guide MLOps practices, and collaborate with data scientists, engineers, product teams, and business stakeholders to maximize business value from AI initiatives.

You will work on cloud-native architectures across AWS/GCP/Azure, design reusable reference pipelines, and mentor a high-caliber analytics team

Qualifications

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.

Responsibilities

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