Senior AI/ML Engineer: Real-Time Personalization & Graphs

BrickRed Systems; Frisco (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

BrickRed Systems is seeking a Senior AI/ML Engineer to lead scalable AI/ML solutions for real-time personalization and recommendations. You will drive end-to-end ML systems, from feature engineering to deployment, working across ML engineering, data engineering, MLOps, and product teams.

The role emphasizes production-grade systems, knowledge graphs, and strong collaboration with cross-functional teams to optimize CTR, engagement, and business KPIs.

Qualifications

• Strong hands-on experience in AI/ML Engineering and production machine learning systems.
• Hands-on experience with Pandas and PySpark.
• Experience with collaborative filtering, content-based recommendations, hybrid models, matrix factorization, deep learning, and ranking models.
• Strong experience with Graph Modeling and graph databases such as Neo4j or Amazon Neptune.
• Experience with ML lifecycle, experimentation, model evaluation, and deployment.

Responsibilities

• Design and build collaborative, content-based, and hybrid recommendation systems.
• Develop real-time personalization pipelines and ranking models.
• Architect and implement end-to-end ML systems for batch and low-latency streaming inference.
• Build and maintain customer knowledge graphs using Neo4j and Neptune.
• Collaborate with Product, Data Engineering, Business, and other stakeholders to translate requirements into scalable AI/ML solutions.
• Mentor engineers and contribute to technical design and architecture decisions.

Skills

AI/ML Engineering

Python

Pandas

PySpark

Graph Modeling

Recommendation Systems

Entity Resolution

ML Lifecycle

Kafka

Tools

Neo4j

Amazon Neptune

Kafka

Spark

Job description

BrickRed Systems is seeking a Senior AI/ML Engineer to lead scalable AI/ML solutions for real-time personalization and recommendations. You will drive end-to-end ML systems, from feature engineering to deployment, working across ML engineering, data engineering, MLOps, and product teams.

The role emphasizes production-grade systems, knowledge graphs, and strong collaboration with cross-functional teams to optimize CTR, engagement, and business KPIs.