Machine Learning Architect

Tiger Analytics Inc.; New Jersey; On-site; USD 120,000 - 160,000; Full time

Job summary

Tiger Analytics Inc. is seeking an experienced professional to design and define system architecture for ML and AI-driven solutions. The role includes leading system design discussions, deploying ML & data pipelines, and collaborating with an Agile team to enhance big data applications.

Required qualifications include a Master’s or doctoral degree in a relevant field and significant hands-on experience with MLOps tools and cloud platforms.

This position offers an opportunity for career development in a challenging entrepreneurial environment.

Qualifications

• Typically requires 10+ years of hands-on work experience in advanced analytics.
• At least 4 years of experience programming with Python.
• Strong programming skills in ML libraries.

Responsibilities

• Design and define system architecture for ML and AI solutions.
• Lead ML system design discussions for model serving and data pipelines.
• Deploy and manage machine learning & data pipelines in production.

Skills

Python programming

MLOps tools

AWS/GCP/Azure

CI/CD pipelines

Data engineering

Model observability

Communication skills

Teamwork

Education

Master's or doctoral degree in computer science or related field

Tools

scikit-learn

TensorFlow

PyTorch

MLflow

Kubeflow

Airflow

SageMaker

Vertex AI

Job description

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.