SSE- AI

Job description

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.