Description

Papaya Global is a rapidly growing, award-winning B2B tech unicorn with an ambitious mission to revolutionize the payroll & payments industry. With over $400M raised from multiple tier-one investors, our innovative technology provides a comprehensive solution for managing global workforces, encompassing everything from hiring and onboarding to managing and paying employees in over 160 countries.

We’re looking for a Senior AI Engineer to join our team and help build cutting-edge solutions at the intersection of Generative AI, traditional Machine Learning, and software engineering. This is an exciting opportunity to work on transformative projects that shape the future of intelligent systems.

What You’ll Do:

• Design, develop and deploy Generative AI systems, including LLM-based pipelines, multi-agent systems, and retrieval-augmented generation (RAG).
• Design, develop and deploy Generative AI and ML pipelines for tasks like classification, clustering, ranking, and recommendation.
• Lead experiments and evaluate models using appropriate benchmarks and metrics.
• Collaborate cross-functionally with product and other R&D teams to bring intelligent features into real-world applications.
• Write clean, efficient, and well-tested Python code to support experimentation, training, and production use.
• Contribute to engineering best practices and knowledge sharing.

Requirements

• 5+ years of experience in AI/ML engineering.
• 3+ years in Python with strong software engineering principles.
• Solid foundation in data structures, algorithms, and design patterns.
• Hands-on experience with LLMs, vector search, embeddings, and modern Gen AI workflows.
• Familiarity with cloud platforms (AWS, GCP, Azure) and modern AI toolkits (LangChain, LangGraph, etc.) is a plus.
• Strong communication and collaboration skills.

Nice to have

• Experience with multi-agent systems, RAG pipelines, or knowledge graphs.
• Experience with ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or similar.
• Exposure to DevOps/MLOps and continuous model delivery.