• Design, build and release real-time decisioning systems that learn from interaction feedback at scale. • Own the modeling logic from how we represent users and signals to how reward attribution closes the loop and improves the next interaction. • Move ideas from papers and notebook prototypes including online learning policies and counterfactual estimators to production code that runs behind real-time APIs. • Design and build resilient streaming/batch pipelines that feed user state, reward signals, and offline replay. • Run honest experiments, A/B test what you ship, design offline evaluation for what you cannot, and kill your own work. when it doesn’t outperform against the baseline. • Continuously monitor and improve the quality, latency, observability, and scalability of the systems. • Collaborate across platform and product teams to turn research-grade ideas into production-grade products. • Share context, mentor engineers, raise the technical bar of the team, and help set direction for how we do ML at scale.

• Lead the transition of machine learning models from theoretical prototypes into scalable, high-performance production systems. • Architect and deploy ML solutions utilizing AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines. • Optimize PyTorch models for production deployment by converting them to ONNX formats. • Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient attention mechanisms like Flash Attention to minimize latency and maximize throughput. • Champion infrastructure best practices for machine learning systems, establishing reliable CI/CD pipelines, and ensuring robust, secure, and reproducible deployments across the AWS ecosystem. • Design, develop, and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured data. • Write clean, efficient, and well-tested code. Complete rigorous testing, debugging, and documentation to ensure seamless installation and long-term maintenance. • Actively participate in research discussions, requirements gathering, and system design alongside domain experts to build tailored scoring and ML solutions.

• Build and Productionize Models: Design, train, and deploy models that directly power our marketing-focused products, primarily for marketing use cases. • Develop Scalable ML Infrastructure: Architect and maintain robust, scalable, MLOps pipelines to ensure reliable training, serving, and monitoring of models in production. • Experiment & Optimize: Drive continuous improvement using A/B testing, uplift modeling, causal inference, and other advanced experimentation frameworks to validate and refine model performance. • Collaborate & Mentor: Work closely with cross-functional teams, including the CEO and CTO, to align on product goals and foster best practices for machine learning and data engineering across the organization.

• Author the AI/ML Architecture Design Document • Recommend an accuracy threshold for the catalog mapping engine. • Build the 4-tier catalog code mapping engine for SAP catalog types. • Implement the AI anomaly detection module. • Design and implement the "Train Model" feedback loop where operator corrections retrain the model. • Wrap the model as a microservice for the main app.