Senior Machine Learning Engineer

Description Nexite is transforming physical retail with real-time product intelligence. Our platform connects products, stores, and customer behavior to help retailers understand what is happening on the sales floor, identify opportunities, and drive better business decisions. We are looking for a Senior Machine Learning Engineer to join our Data & AI team and help build scalable ML systems that turn complex retail data into production-grade intelligence. As a Senior Machine Learning Engineer, you will own the path from data and model development to reliable production systems. You will work closely with Data Scientists, Data Engineers, Software Engineers, Product Managers, and business stakeholders to design, build, deploy, and scale ML-driven capabilities across Nexite’s platform. This role is ideal for someone who combines strong engineering foundations with practical ML experience, enjoys solving real-world data problems, and knows how to turn experimentation into robust, maintainable production systems. Responsibilities Design, build, optimize, and maintain scalable ML and data pipelines using Apache Airflow. Productionize machine learning models as reliable batch processes, services, APIs, and data products. Build and maintain infrastructure for model training, validation, deployment, monitoring, and continuous improvement. Work with large-scale retail, product, movement, and behavioral datasets to generate actionable insights, recommendations, and business signals. Collaborate with Data Scientists to translate research and prototypes into scalable, tested, and maintainable production code. Design data models, write efficient queries, and optimize performance across BigQuery, PostgreSQL, and MySQL. Package, deploy, and manage ML services and applications using Docker and cloud-native infrastructure. Work primarily with Google Cloud Platform, including services such as BigQuery, GKE, Vertex AI, Cloud Run, and related tools. Explore and implement practical AI and LLM-based workflows where they can improve automation, decision-making, or internal productivity. Lead technical design discussions, perform code reviews, improve engineering standards, and mentor other team members. Requirements 5+ years of professional experience as a Machine Learning Engineer, Data/ML Platform Engineer, Backend Engineer working on ML systems, or a similar role. Strong hands-on experience building production-grade ML, data, or analytics pipelines. Advanced Python skills and strong familiarity with the ML/data ecosystem, such as Pandas, scikit-learn, PyTorch, TensorFlow, or similar tools. Proven experience designing and operating complex workflows with Apache Airflow. Strong SQL skills and experience working with relational databases such as PostgreSQL and/or MySQL. Experience working with large-scale analytical data stores, preferably BigQuery. Solid experience deploying and operating applications in cloud environments, preferably GCP. Practical experience with Docker and modern software engineering practices, including testing, version control, code reviews, CI/CD, and observability. Ability to take ownership of ambiguous technical problems and drive them from design to production. Strong communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders. Nice to Have Experience with Vertex AI, GKE, Cloud Run, or other GCP ML/platform services. Experience building recommendation systems, anomaly detection systems, signal detection engines, optimization models, or decision-support systems.