ML Ops Engineer

As Relyance AI’s ML Ops Engineer, you will develop machine learning infrastructure components with a data-centric mindset to ease iteration, evaluation, and deployment of machine learning models. Partnering with machine learning and backend engineers, you will deliver scalable, performant, highly available data ingestion and processing pipelines to drive business impact. In this role, you’ll have full ownership to design features from scratch and will be constructing the foundation on which our global data infrastructure will be built. 

As a ML Ops Engineer, your role will include:

• Developing scalable and reliable machine learning infrastructure components to ease iteration, evaluation, and deployment of machine learning models.
• Designing data ingestion and processing ETL pipelines for scale with tools such as Airflow, and cloud-based data services like Google’s BigQuery, BigTable, and Pub/Sub.
• Designing and evolving data models according to business and engineering needs.
• Collaborating closely with our machine learning and core backend teams to drive maximum impact across the organization.
• Participating in ensuring compliance with privacy by design principles (e.g., design data de-identification pipelines to develop systems that preserve customer privacy protections).
• Following and promoting software engineering and data engineering best practices across the organization; keeping up to date with the state-of-the-art developments in data engineering open-source frameworks and MLOps.
• Shaping the direction of data engineering at Relyance and building a cohesive team culture of ownership, growth, transparency, and customer focus.

This role could be a fit for you if you bring:

• A track record of delivering scalable and reliable machine learning infrastructure components and data ingestion and processing pipelines. 
• Experience with Python. 
• Strong belief in data-centric — as opposed to model-centric — MLOps.
• Ability to write clear, concise, and maintainable code considering design principles and applying sound testing practices.
• Experience in designing and evolving data models and ETL pipelines with job orchestration tools like Airflow. 
• Proficient with public cloud concepts and delivering working solutions on public cloud infrastructure, preferably GCP (BigQuery, BigTable, Pub/Sub).
• A deeply curious mindset, proactive about continuous improvement, and excitement for learning quickly in a fast-growth environment.
• The ultimate team player: collaborate effectively with others, consistently make time to help your teammates, and are ego-less in the search for the best ideas.

Bonus points for:

• Customer and mission-driven: motivated by bringing the most value as possible to users and shaping an industry from the ground up
• Close attention to detail and a forward-thinking outlook.
• Excitement for a fast-paced, iterative, but heavily test-driven development environment.

Who are we?

Using machine learning, Relyance AI builds a dynamic, real-time data inventory and map so you can monitor how personal data moves through your code, applications, infrastructure, and to third-party vendors. We exist because we believe innovation is fundamental to human progress. We build for people—our customers, our team, and the global community. Our core values brought us into existence, and they’re the motor that keeps us running.

GLOBAL IMPACT | Think big. Beyond borders. 

TRUST | Lost easily. Built carefully. 

CRITICAL THINKING | Critical eye. Critical mind. 

CUSTOMER | Redefine satisfaction. Build loyalty. 

PERSISTENCE | Celebrate failure. Keep going. 

TEAM | Fast alone. Far together. 

DIVERSITY | We win through diversity. 

Relyance AI is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.