Principal / Staff Software Engineer - Backend, MLOps and Cloud Infrastructure

About Seed-X: Seed-X is an innovative, high-growth AgTech company revolutionizing the AgriTech industry. We leverage proprietary AI, machine vision, and deep learning algorithms to analyze seeds at the individual level, providing unmatched insights into genetic purity, quality, and germination potential. Our technology enables breeders and producers to drastically improve seed quality, increase yields, and ensure a more secure and sustainable food future. Join us as we transform the global seed and grain supply chain. About the Role Seed-X is looking for a visionary Principal / Staff Software Engineer to own, architect, and elevate our backend ecosystems, machine learning infrastructure, and cloud platforms. In this dual-impact role, you will act as a premier individual contributor and a technical beacon for the engineering team. You will bridge the gap between advanced backend engineering, scalable MLOps, and robust cloud infrastructure, ensuring our production-grade platforms are highly available, observable, secure, and built to scale. If you thrive on solving complex distributed systems problems and setting the gold standard for engineering excellence, we want you on board. Key Responsibilities Technical Leadership & Architecture Lead the architectural design, evolution, and scaling of our distributed backend microservices and machine learning platforms. Establish, champion, and enforce engineering best practices across the organization, including rigorous code reviews, automated testing, design docs, and security protocols. Mentor and coach senior engineers, fostering a culture of technical curiosity, continuous learning, and high execution velocity. Backend & Infrastructure Engineering Design and implement high-performance, resilient, and secure cloud-native solutions using Python and modern web application frameworks. Architect and optimize data layers, ensuring database designs (SQL and NoSQL) are tuned for high throughput, low latency, and long-term scalability. Drive the containerization and orchestration strategy using Docker and Kubernetes to ensure seamless deployments and efficient resource utilization. MLOps & Cloud Operations Build and maintain robust MLOps infrastructure to streamline the entire ML lifecycle—from data pipelining and model training to production deployment and monitoring. Own and secure our AWS cloud infrastructure, optimizing complex networking setups including VPCs, DNS, ingress controllers, and service meshes. Architect end-to-end CI/CD pipelines and DevOps automation to facilitate reliable, friction-free daily deployments. Cultivate absolute system visibility by implementing comprehensive observability stacks (Datadog, Prometheus, Grafana, OpenSearch/ELK) and distributed tracing. Core Development: Mastery of Python and deep experience building enterprise-grade backend systems and web applications. Distributed Systems: Proven track record in microservices architecture, API design, asynchronous event-driven patterns, and system scalability. Cloud & Networking: Deep, hands-on expertise with AWS cloud infrastructure, networking configurations (VPCs, DNS, Ingress, Service Mesh), and infrastructure as code. Containers & Orchestration: Production-level mastery of Docker and Kubernetes for scaling and managing containerized workloads. Data Management: Strong proficiency in database design, query optimization, and handling complex data lifecycles. MLOps Foundations: Experience building or integrating machine learning infrastructure, model deployment pipelines, and ML feature stores. Observability & DevOps: Expert knowledge in deploying logging, metrics, and tracing stacks ( Datadog, Prometheus, Grafana, ELK ) alongside bulletproof CI/CD pipelines. Qualifications & Experience Experience: 9+ years of professional software engineering experience, with at least 3+ years spent operating at a Staff, Principal, or equivalent technical leadership capacity. Track Record: Demonstrated success in designing and operating large-scale distributed cloud systems from the ground up. Mindset: A strong sense of ownership, a passion for automation, and the communication skills required to translate complex technical visions into actionable team execution. Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience). Competitive salary and equity packages. The opportunity to significantly shape the technical trajectory of an innovative, growing company. Remote work and flexibility. Budget for continuous learning, conferences, and professional development.