ML Ops / Data Engineer

Point Wild helps customers monitor, manage, and protect against the risks associated with their identities and personal information in a digital world. Backed by WndrCo, Warburg Pincus and General Catalyst, Point Wild is dedicated to creating the world’s most comprehensive portfolio of industry-leading cybersecurity solutions. Our vision is to become THE go-to resource for every cyber protection need individuals may face - today and in the future.

Join us for the ride!

About the Role:

Point Wild is building the Trailblazer AI Team—a dedicated group focused on applying AI to enhance our products, drive business growth, and deliver real-world impact across multiple product lines (antivirus, VPN, identity protection, password management, and more).

As an ML Ops / Data Engineer, you will play a critical role in standing up and maintaining the data pipelines and infrastructure that power AI initiatives across the company.

This role is a hybrid between data engineering and MLOps—ensuring that AI engineers have clean, structured data for modeling while also building and managing the ML infrastructure for deployment, scaling, and monitoring.

Day to Day:

• Data Pipeline Development – Design and maintain ETL/ELT pipelines to ingest, clean, and transform data from multiple product lines.
• AWS ML Infrastructure – Stand up and manage AWS-based ML infrastructure (e.g., S3 data lakes, AWS Glue, EMR, AWS Batch, Lambda, SageMaker).
• Model Deployment & MLOps – Own CI/CD for ML models, including environment setup, model versioning, containerization, and monitoring.
• Support AI Engineers – Ensure AI teams have reliable access to data, scalable training environments, and efficient deployment pipelines.
• PoC to Production Scaling – Help move AI proofs-of-concept from experimentation to fully productionized, scalable deployments.

Why this role matters?

• Enables AI at Scale – AI engineers are focused on modeling; we need dedicated expertise in data pipeline engineering and infrastructure.
• Critical to Operationalization – AI models are only valuable if they can be deployed, scaled, and monitored in production.
• Bridges Data & AI – This role ensures structured, clean, and usable data flows into AI initiatives while also handling model deployment.

What you bring to the table:

• Data Engineering Expertise – Experience building and maintaining ETL/ELT pipelines for large-scale data ingestion and transformation.
• Cloud & MLOps Proficiency – Strong knowledge of AWS services for ML infrastructure, model deployment, and automation.
• DevOps & CI/CD – Experience setting up CI/CD workflows for ML models, including versioning, monitoring, and automated retraining.
• Python & SQL Skills – Comfortable writing efficient Python and SQL scripts for data processing and model deployment.
• Practical, Execution-Oriented – Can balance quick PoC enablement with long-term scalability in AI deployments.

Why Join Us?

• Work on Cutting-Edge AI: Build and optimize real-world AI solutions across multiple security-focused product lines.
• Be a Key Player in AI Deployment: Own critical aspects of model development, productionization, and optimization.
• Learn & Grow in a High-Impact Team: Collaborate with top AI engineers and scale AI innovation within a growing company.