Machine Learning Engineer

Tiger Analytics is a global AI and analytics consulting firm. With data and technology at the core of our solutions, we are solving problems that eventually impact the lives of millions globally. Our culture is modeled around expertise and respect with a team-first mindset. Headquartered in Silicon Valley, you’ll find our delivery centers across the globe and offices in multiple cities across India, the US, UK, Canada, and Singapore, including a

substantial remote global workforce.

We’re Great Place to Work-Certified™. Working at Tiger Analytics, you’ll be at the heart of an AI revolution. You’ll work with teams that push the boundaries of what is possible and build solutions that energize and inspire.

We are seeking a highly skilled MLE / MLOps Engineer with a strong programming background and solid experience in software engineering practices. The ideal candidate will play a critical role in building and maintaining robust machine learning infrastructure, ensuring seamless integration between ML models and production systems.

Requirements

• Design, implement, and maintain scalable and reliable MLOps pipelines for model training, deployment, and monitoring.
• Collaborate with data scientists and software engineers to productionize ML models.
• Develop and maintain CI / CD workflows for ML systems and model lifecycle management.
• Work with real-time data using Apache Spark Streaming to support high-throughput data processing pipelines.
• Ensure high availability and performance of ML services in production.
• Manage and automate infrastructure using tools such as Docker, Kubernetes, and Terraform.
• Monitor and improve system performance, model drift, and data quality issues.
• Implement best practices in software engineering including code reviews, testing, and documentation.

Required Qualifications

Preferred Qualifications

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.

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