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Tier-1 brand, mid-level experience, broad ML skillset, and Bangalore location increase candidate competition.
ML engineering skills are transferable across industries, though retail promo expertise is advantageous.
Explicit 4+ years and mandatory ML/MLOps production experience enforce strict technical filters.
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Build and scale production-grade AI/ML systems for promo optimization and personalized marketing using Python and modern engineering practices.
Design and operate scalable data and ML pipelines, APIs, and inference systems for promotion decisioning, segmentation, offer ranking, and campaign simulation.
Implement MLOps capabilities including CI/CD, model monitoring, lifecycle management, and incorporate emerging AI technologies like Generative AI and LLMs for retail solutions.
Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, Mathematics, Statistics, or related field, or equivalent practical experience.
4+ years of experience in software engineering, AI engineering, machine learning engineering, data engineering, MLOps, or production ML systems.
Strong hands-on programming experience in Python with production-quality coding practices and ability to build end-to-end AI/ML pipelines.
Experience with distributed data platforms (e.g., Spark, Hadoop/Hive), SQL, multiple database technologies, APIs, and modern DevOps practices for ML deployments.
Experienced in developing scalable, reliable AI systems at the intersection of AI, software engineering, data platforms, and MLOps, with strong software engineering fundamentals.
Comfortable collaborating with Data Scientists and cross-functional teams to transition models and prototypes into production-ready, maintainable systems.
Proficient in integrating emerging AI technologies such as Generative AI, LLMs, and agentic AI workflows to solve complex retail business problems at scale.