Job Description

As an AI Implementation Engineer, you will be responsible forintegrating, optimizing, and deploying AI/ML solutions that enhance banking intelligence, automation, and fraud detection. Whether youre working on backend systems, frontend AI-powered interfaces, or full-stack AI implementations, your contributions will help shape the future of AI-driven banking. Responsibilities Develop & deploy AI/ML models for banking, payments, and fraud detection. Integrate AI-powered decision-making into real-time banking systems. Optimize AI pipelines for scale, latency, and security. Leverage NLP, Computer Vision, or Generative AI to build intelligent banking solutions. Work with engineering teams to productionize AI solutions within Zeta s platform. Use MLOps best practices to ensure seamless AI model deployment, monitoring, and scaling. Mine TBs of transaction data to generate insights and improve predictive capabilities. Experiment with AI-driven user experiences (chatbots, voice assistants, Copilot-like interfaces). Ensure AI models comply with regulatory and security standards. Skills Must have 1-5 years experience in building AI Tools and working on Copilot, PowerApps. Past projects should be included in github. Strong AI/ML experience (training, deploying, and scaling models in production). Proficiency in Python, TensorFlow, PyTorch, or scikit-learn. Experience with ML model deployment (Docker, Kubernetes, MLflow, or similar). Knowledge of MLOps pipelines, versioning, and monitoring. Experience working with large datasets, feature engineering, and model tuning. Understanding of Generative AI, NLP, or Computer Vision. Ability to integrate AI solutions into backend or frontend applications. You must have your github / repo links on your resume to showcase your past projects.