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Tier-1 brand, metro location, and broad ML+software skill requirements increase candidate competition.
Skills are transferable across industries, but production ML focus and payments context increase domain specificity.
Multiple explicit mandatory technical skills (Python, PyTorch/TensorFlow, Java, production ML lifecycle) enforce strict shortlisting.
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Build and own Java services/APIs delivering AI-powered features with performance and maintainability in a distributed environment.
Develop, productionize, and operate AI components in Python, including model lifecycle management, automation, deployment, and monitoring.
Deliver end-to-end across design, development, testing, deployment, and documentation with a focus on engineering excellence and troubleshooting complex AI/software issues.
Strong hands-on Java engineering skills for scalable, reliable software development with testing discipline.
Proven hands-on Python engineering experience for AI workloads including production code and operationalization.
Hands-on experience with modern AI frameworks like PyTorch, TensorFlow, or Hugging Face for building, tuning, or serving models.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in integrating AI engineering with software engineering for production-grade AI systems with scalability and reliability focus.
Able to manage full AI/ML lifecycle delivery including model deployment, workflow automation, monitoring, and operational safeguards.
Capable of mentoring peers technically, driving adoption of best practices, and collaborating effectively across multiple roles and geographies.