AI/ML Developer
Payoneer Global Inc - Gurugram, India
Posted Apr 28, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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Schedule
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- Weekend work
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Application
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About this role
AI/ML Developer Gurugram, India About Payoneer Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world's underserved businesses to a rising global economy. We're a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories. By taking the complexity out of the financial workflows-including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence-we give businesses the tools they need to work efficiently worldwide and grow with confidence. Role Summary We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions - from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned. Key Responsibilities 1 Model Design & Training - Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX. - Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning. - Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters. - Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results. - Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.
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