Machine Learning Engineer

3 weeks ago


Gurugram Gurugram India Nitro Commerce Full time

Job Description Company Description Nitro Commerce offers AI tools to unlock the full potential of India's rapidly growing e-commerce market, with a focus on Direct-to-Consumer (D2C) brands. With a decade of experience in crafting revolutionary marketing technology solutions for leading Indian commerce brands, we understand the challenges faced in this ever-evolving landscape. We equip brands with AI-powered solutions to overcome obstacles such as anonymous visitors, complex marketing, and clunky conversions. Join us on our mission to transform the future of Indian e-commerce by building tools, partners, and growth engines for tomorrow's leaders. What will you work on/Key Responsibilities: - Design, implement, and deploy Machine Learning models for key AdTech functionalities, including: - Click-Through Rate (CTR) and Conversion Rate (CVR) prediction. - Real-time Bidding (RTB) and budget optimization algorithms. - User segmentation and product recommendation specific to quick-commerce buying patterns. - Brand safety and ad quality filtering. - Collaborate with Data Science and Product teams to translate business needs (e.g., maximizing ad spend efficiency, increasing brand visibility) into scalable ML solutions. - Manage the end-to-end ML lifecycle (data processing, feature engineering, model training, deployment, A/B testing, and monitoring) in a high-throughput, low-latency environment. - Optimize model performance and maintain data pipelines using cloud-native technologies What are the perks - Freedom to experiment, fail and learn fast, and build cool things - A healthy work culture that rewards curiosity and out of box thinking - Compensation corresponding to industry standards, experience and technical acumen, with generous ESOPs Who are we looking for - 1+ year of professional experience in Machine Learning - Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). - Experience with large-scale data processing using tools like Spark or similar distributed systems. - Solid understanding of statistical modeling, model evaluation, and techniques for handling imbalanced data. - Familiarity with containerization (Docker, Kubernetes) and MLOps principles.



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