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At True Credits you build resilient AI systems that won't melt under millions of concurrent production requests.\n\nThe Foundations : \n\n- Strong background in fundamentals : concurrency, distributed systems, memory management, data structures, and system design.\n\nTech Stack : \n\n- 5+ years of experience shipping production-grade code.\n\n- Deep mastery of Python and either Go, Java, or C++.\n\n- ML Infrastructure : Hands-on experience with production ML serving frameworks (Triton, TorchScript, BentoML) and MLOps platforms (MLflow, Kubeflow).\n\n- You know how to build CI/CD for ML.\n\n- You don't just write code; you write automated tests for model behavior, input validation, and fallback mechanisms.\n\nRoles and responsibilities : \n\n- Architect Inference Infrastructure : Own the end-to-end performance of our LIP execution layer.\n\n- You will design and build highly scalable microservices that serve models, ensure predictive accuracy, and maintain a strict execution budget per request.\n\n- Engineer Real-Time Feature Streaming : Design, optimize, and maintain distributed, event-driven data pipelines (using Flink, Kafka, Spark) to extract and aggregate alternative data features on the fly.\n\n- You will turn chaotic user activity streams into clean, structured inputs for our models in real time.\n\n- Build Next-Gen MLOps Ecosystems : Develop automated, robust CI/CD frameworks specifically for machine learning.\n\n- This includes building self-healing pipeline abstractions, automated model retraining loops, and automated shadow-testing frameworks where new models can safely run alongside production models before a full rollout.\n\n- Eliminate Drift and Data Skew : Implement rigorous monitoring, alerting, and automated validation systems to catch training-serving data skew, input anomalies, and feature drift before they impact underwriting or risk decisions.\nSkills\nMachine Learning, MLOps, Python, Java, C++, Apache Flink, Kafka, Golang, Cloud Architect - 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