Rpa Engineer , Ar Automation

Amazon Amazon · Big Tech · IN, TS, Hyderabad · Operations, IT, & Support Engineering

This role focuses on designing and implementing intelligent automation solutions using AI, ML, LLMs, and RPA tools within Amazon's Accounts Receivable team. The engineer will optimize existing automations, partner with stakeholders, lead projects, and ensure reliability and integration across financial systems. Basic qualifications include RPA certifications, programming experience (Python/C#), and experience with RPA tools and APIs. Preferred qualifications involve deploying AI products at scale, data mining, and integrating LLMs with enterprise data using embeddings and vector search.

What you'd actually do

  1. Design and develop automation solutions using AI, ML, LLMs, AWS services, and RPA tools like UiPath
  2. Optimize and maintain existing automations, including SQL query optimization and database management
  3. Partner with stakeholders to gather requirements, create technical specifications, and ensure solutions meet business objectives
  4. Lead multiple concurrent automation projects from inception to deployment while meeting established timelines
  5. Implement robust error handling, monitoring, and testing procedures to ensure automation reliability

Skills

Required

  • UiPath Certified Professional Developer certification
  • Experience programming with at least one software programming language
  • experience in developing and deploying LLMs in production on GPUs, or other AI acceleration hardware
  • Hands-on experience with UiPath, Automation Anywhere, or Blue Prism
  • Experience in Python (for AI libraries) and/or C#/.NET (for RPA framework customization)
  • Experienced in consuming RESTful APIs, JSON, XML, and SQL querying

Nice to have

  • Experience developing, deploying and managing AI products at scale
  • Experience in creating process improvements with automation and analysis
  • experience working with large-scale data mining and reporting tools (i.e. SQL, MS Power Query, Python)
  • Experience using data to drive root cause elimination and process improvement
  • Integrate LLMs with private enterprise data sources (PDFs, databases, internal documents) using embeddings and vector search techniques to retrieve contextually relevant information and ground AI outputs in factual data.

What the JD emphasized

  • deploying LLMs in production on GPUs, or other AI acceleration hardware
  • Experience developing, deploying and managing AI products at scale
  • Integrate LLMs with private enterprise data sources (PDFs, databases, internal documents) using embeddings and vector search techniques to retrieve contextually relevant information and ground AI outputs in factual data.

Other signals

  • AI/ML/LLMs
  • RPA
  • automation
  • large-scale