JOB DESCRIPTION About the Role We’re looking for a Machine Learning Engineer who thrives on end-to-end ownership of machine learning systems — from problem framing and model development to deployment and monitoring in production. Unlike traditional roles that split responsibilities between Data Scientists and Engineers, this position is designed for hands-on builders who can independently develop, validate, and scale models that deliver real business impact. Responsibilities Design, train, and evaluate models independently using structured and unstructured datasets Build scalable and robust ML pipelines for training, validation, and continuous delivery Develop and own production-grade model serving systems, including monitoring and retraining workflows Write clean, maintainable code and establish patterns and best practices for ML development Collaborate with stakeholders to define success metrics and measure model impact Lead or contribute to architectural decisions and long-term technical strategy QualificationsMinimum Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Math, or related field 3+ years of hands-on experience building ML models independently and deploying them to production Deep understanding of machine learning algorithms and statistical modeling techniques Proficiency in Python and ML frameworks (., scikit-learn, PyTorch, XGBoost, TensorFlow) Experience with the full ML lifecycle, including model training, validation, and serving Solid engineering skills, including writing reusable code, working with APIs, and CI/CD Familiarity with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) Preferred : Identify opportunities to apply machine learning and data-driven solutions across product and platform areas Experience with online model inference and real-time decision systems Exposure to feature engineering at scale and feature store design Knowledge of model observability, performance tracking, and drift detection Experience deploying models using FastAPI, Flask, TorchServe, or equivalent