Principal Engineer, Data & ML Platform at Encora10

We are seeking an experienced Principal / Senior Software Engineer, Data & ML Platform to lead the design and development of the foundational data and platform systems that power our AI and machine learning products. This is a hands-on technical leadership role responsible for architecting scalable, cloud-native infrastructure that enables real-time and batch data services for training, inference, and feedback loops. You will help define the technical direction of our data and ML platform while partnering closely with AI Platform, MLOps, and backend engineering teams. Key Responsibilities • Architect and build the core data and software systems that support our AI and ML platforms. • Design and develop scalable data pipelines, APIs, and platform services for ML and AI use cases. • Build real-time and batch data frameworks to support model training, inference, and continuous feedback loops. • Drive platform scalability, reliability, and engineering excellence across distributed systems. • Partner with cross-functional engineering leaders to shape long-term platform strategy and technical direction. • Mentor engineers and establish best practices for system design, testing, deployment, and performance optimization. • Evaluate and implement technologies that improve platform capability, reliability, and developer efficiency. Qualifications • Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred. • 7+ years of experience in software engineering, data infrastructure, ML platform, or related engineering roles. • Strong expertise in Python, Java, and/or PySpark. • Proven experience designing and scaling cloud-native data and ML systems, preferably in GCP and Databricks. • Deep understanding of distributed systems, system design, data modeling, and platform architecture. • Strong experience building and operating large-scale, data-intensive systems. • Excellent technical leadership, communication, debugging, and problem-solving • Experience with automated testing, deployment best practices, and production-grade software development.