Tiger Analytics

Delivers Data and AI transformation

Machine Learning Architect

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeRemote in USANo salary listed

About the Job

Requirements

Typically requires 10+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment, including at least 4 years programming with Python.
At least 7 years of experience productionizing, monitoring, and maintaining models.
Strong programming skills in Python and machine learning libraries such as scikit-learn, TensorFlow, and PyTorch.
Deep experience with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, or Vertex AI.
Hands-on experience designing machine learning systems using cloud platforms such as AWS, Azure, or GCP.
Strong understanding of data engineering, APIs, CI/CD pipelines, and model observability.
Excellent communication and stakeholder management skills.
Excellent communication and teamwork skills.
Ability to work with a global team and communicate problem context to remote teams.

Responsibilities

Design and define system architecture for machine learning and AI-driven solutions across multiple business verticals.
Lead machine learning 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 machine learning model training, validation, deployment, and monitoring using AWS, GCP, or Azure.
Build reusable components and reference architectures for various stages of the machine learning lifecycle.
Define and enforce best practices in model versioning, CI/CD for machine learning, testing, and rollback strategies.
Deploy and manage machine learning and data pipelines in production environments.
Work on containerization and orchestration solutions for model deployment.
Participate in fast iteration cycles and adapt to evolving project requirements.
Collaborate as part of a cross-functional Agile team to create and enhance software that enables advanced big data and machine learning applications.
Collaborate with data scientists, software engineers, data engineers, and other stakeholders to develop and implement MLOps best practices, including CI/CD pipelines, version control, model versioning, monitoring, alerting, and automated model deployment.
Collaborate with data scientists, engineers, product teams, and business stakeholders to architect robust, scalable solutions aligned with business goals.
Collaborate with cross-functional teams and business partners, leverage analytical skills to drive current and future strategy, ensure business value, and communicate results.

Machine Learning Architect

Tiger Analytics Inc. · United States (Remote) · Full-time · Posted 2025-07-03

Workplace: remote

Department: MLE

Description

Tiger Analytics is looking for an experienced Principal Data Scientist to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

Highly experienced Machine Learning Architect with a proven track record of designing and delivering end-to-end ML solutions across diverse business domains. The ideal candidate will have over 10 years of experience in data science, machine learning, and MLOps, and a deep understanding of scalable system design, model lifecycle management, and production-grade deployment pipelines.
This is a strategic and hands-on role, involving collaboration with data scientists, engineers, product teams, and business stakeholders to architect solutions that are robust, scalable, and aligned with business goals
You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

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.

Apply

Apply at Tiger Analytics Inc.

About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: San Jose, CA
Founded: 2010

Recently Posted Jobs

Explore more opportunities at Tiger Analytics

See more jobs