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Tier-1 brand plus in-demand ML/AI skills create moderate candidate competition.
Core ML engineering skills are transferable, but payments domain and enterprise Java constraints increase specificity.
Multiple mandatory technical requirements (Python, AI frameworks, Java, production ML lifecycle) raise strictness.
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Develop and own Java services/APIs delivering AI-powered features ensuring performance, correctness, and maintainability in a distributed environment.
Build, productionize, and operate AI components in Python including model lifecycle management, automation, deployment, and monitoring for sustained high-quality outputs.
Lead end-to-end delivery including design, development, testing, deployment, configuration, and documentation with strong engineering discipline and troubleshooting of complex AI and service issues.
Proven hands-on Python engineering experience for AI workloads, including production code, tests, packaging, and operationalization.
Hands-on experience with modern AI frameworks such as PyTorch, TensorFlow, or Hugging Face for building, tuning, or serving models.
Strong Java engineering skills with experience designing, coding, testing, maintaining scalable and reliable software solutions.
Work Experience Required: Not explicitly mentioned in the JD.
Strong blend of software engineering skills for deterministic, reliable services and AI engineering expertise handling probabilistic systems and AI model lifecycle.
Experience operating production AI/ML workflows including deployment automation, monitoring, and model versioning to ensure robustness and business-aligned safeguards.
Demonstrated capability to own complex AI-enabled software components end-to-end, driving engineering excellence and mentoring peers without people management responsibilities.