Platform Engineer

Software Engineer AI

About Netcore UNBXD

Netcore Cloud is a global Martech and Product Discovery leader, enabling brands to deliver intelligent, hyper-personalized customer experiences at scale. Our platform processes billions of events daily, combining advanced data engineering, real-time decisioning, and machine learning to power high-performance SaaS solutions used by thousands of enterprises worldwide.

We operate at the intersection of distributed systems, AI innovation, and large-scale infrastructure — solving complex engineering problems that directly impact global businesses.

The Opportunity

We are looking for a high-impact AI Engineer who thrives in solving large-scale data challenges. You will play a key role in architecting and building intelligent systems that power personalization, automation, and predictive insights across our Martech ecosystem.

This role demands strong engineering fundamentals, system-level thinking, and a deep interest in scalable AI systems.

Key Responsibilities

• Architect and develop scalable, high-throughput Data and ML pipelines.
• Design real-time and batch processing systems for large-scale event streams.
• Build robust, production-grade ML workflows and inference systems.
• Optimize distributed systems for performance, reliability, and cost efficiency.
• Collaborate cross-functionally with Product, Analytics, and Platform teams to deliver intelligent features.
• Contribute to system design discussions and drive engineering best practices.

Core Requirements

• Strong proficiency in Java, Golang, or Python.
• Hands-on experience in data engineering and ML systems development.
• Deep understanding of distributed systems, scalability, and performance tuning.
• Experience with big data frameworks such as Spark, Storm, or Apex.
• Strong foundation in data structures, algorithms, and system design.
• Experience building production-ready systems in cloud environments.

Preferred Qualifications

• Experience with Kafka, Hadoop, Hive, Vertica, or Spark Streaming.
• Exposure to containerization and orchestration tools like Docker and Kubernetes.
• Familiarity with ML lifecycle tools such as Kubeflow or AWS Batch.
• Experience with real-time personalization, recommendation systems, or predictive analytics.

What Makes This Role Exciting

• Work on AI systems operating at massive scale.
• Influence architectural decisions in a rapidly growing SaaS company.
• Solve real-world engineering challenges impacting global enterprises.
• High ownership, high visibility, high growth.