Machine Learning Engineer

We are seeking an early‑career Machine Learning Engineer who is excited to grow rapidly by building and deploying production‑grade ML systems. The ideal candidate has a strong engineering mindset, has contributed to shipping ML features or products end‑to‑end, and is eager to take ownership across the full lifecycle—from data pipelines to model design to deployment, monitoring, and iteration in real‑world environments. This role offers hands‑on exposure to applied ML, working with IoT datasets, user needs, and product

to build scalable solutions that deliver measurable customer ROI.

: • Design, build, and deploy ML models into production environments, ensuring reliability, scalability, and performance. • Ability to select and apply the appropriate ML approach for a given problem — including supervised learning (e.g., logistic regression, random forest, gradient boosting), unsupervised learning (e.g., clustering, dimensionality reduction), and deep learning techniques when appropriate. • Develop and maintain feature engineering pipelines, data preprocessing flows, and training workflows. • Collaborate with cross‑functional partners including product, data engineering, DevOps & QA to deliver end‑to‑end ML solutions. • Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML, monitoring/alerting, automated retraining, and model governance. • Continuously evaluate and improve models by monitoring performance, identifying and addressing bias, detecting data or concept drift, and iterating on features, algorithms, or training processes to maintain reliability over time. • Ensure solutions meet security, compliance, and data privacy standards. • Document system architectures, modeling decisions, and operational procedures. • Work in a high performing scrum team to deliver quality code for stakeholders.