Job description
Toters is an on-demand e-commerce and delivery platform and operates a service that enables customers to get anything in their city at the highest level of convenience. At Toters, technology is at the heart of everything we do. We have product teams that are working hard every day to create products that make our customers' lives easier. Our engineers are also continuously creating solutions to make our processes more efficient, all in an effort to get to our customers fast and at the best cost.\n\nAbout the Role\nWe are seeking a hands-on experienced Mid ML/AI Engineer to join our Product Platform Engineering team. You will design and lead production-grade machine learning systems supporting quick commerce and fintech platforms. The role involves owning the full lifecycle of ML models, working with large-scale datasets (10TB+), and driving strategic AI initiatives that create measurable business impact.\n\nKey Responsibilities\n- Build and maintain end-to-end ML pipelines using XGBoost, LightGBM, PyTorch, and TensorFlow.\n- Architect scalable training and inference systems for real-time predictions.\n- Lead cross-functional ML initiatives and mentor engineering teams.\n- Optimize models for demand forecasting, fraud detection, and personalization.\n- Apply MLOps best practices and collaborate with DevOps on deployment.\n- Process and analyze 10TB+ datasets using distributed computing frameworks.
Requirements
- 4+ years building production ML systems at scale (100K+ users).\n- Expert in XGBoost, LightGBM, and gradient boosting algorithms.\n- Strong Python and ML stack experience: PyTorch, TensorFlow, NumPy, Scikit-learn.\n- Hands-on experience with agentic AI systems and MCP frameworks.\n- Proven track record in ML deployment and MLOps.\n- Technical leadership and mentorship experience.\n\nPreferred Qualifications\n- Proficiency in Python.\n- Experience in quick commerce or fintech ML applications.\n- Familiarity with cloud platforms, microservices, or distributed systems.\n- Research, publications, or open-source contributions in ML/AI.