Sr. AI Process Engineer, Seller Compliance

Amazon Amazon · Big Tech · 31, China +1 · Project/Program/Product Management--Non-Tech

Senior Process Engineer to lead AI-driven engineering initiatives in the Compliance domain, focusing on designing, building, and operating AI-powered solutions to improve Seller compliance outcomes and operational efficiency. Requires deep hands-on expertise in AI/ML development, building, deploying, and scaling production-grade AI systems, designing system architectures, building ML models and data pipelines, and driving technical collaboration. Must be proficient in Python, cloud platforms (AWS), and ML operations, with experience in compliance or regulatory domains preferred.

What you'd actually do

  1. Design and implement scalable AI/ML solutions for Compliance use cases
  2. Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving.
  3. Build robust, production-grade AI services using Python and modern ML frameworks.
  4. Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective.
  5. Establish and maintain high engineering standards, including testing, monitoring, and documentation.

Skills

Required

  • Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, or similar).
  • Demonstrated experience designing, implementing, and operating production AI systems at scale.
  • Hands-on experience with cloud platforms (preferably AWS) and associated ML services.

Nice to have

  • Master’s degree in Computer Science, Machine Learning, or a related field.
  • Experience with NLP, deep learning, or document understanding applications.
  • Experience working with Compliance, risk management, or other highly regulated systems.
  • Proven track record of leading technical teams or initiatives and mentoring engineers.
  • Hands-on experience with ML Ops, including model deployment, monitoring, and lifecycle management.
  • Strong problem-solving, analytical, and troubleshooting skills with a bias for action.

What the JD emphasized

  • AI/ML development
  • building, deploying, and scaling production-grade AI systems
  • Python
  • cloud platforms (e.g., AWS)
  • ML operations
  • compliance or regulatory domains

Other signals

  • AI-driven engineering initiatives
  • AI-powered solutions
  • building, deploying, and scaling production-grade AI systems
  • design system architectures
  • build efficient ML models and data pipelines
  • drive technical collaboration
  • ensure AI systems are robust, maintainable, and secure
  • translating business requirements into scalable solutions
  • mentoring engineers
  • driving technical excellence
  • Python, cloud platforms (e.g., AWS), and ML operations is essential
  • shape the future of Compliance via innovative AI engineering solutions
  • scalable AI/ML solutions for Compliance use cases
  • efficient ML models and end‑to‑end data processing pipelines from ingestion to serving
  • production-grade AI services
  • architectural decisions
  • systems are scalable, secure, and cost‑effective
  • high engineering standards, including testing, monitoring, and documentation
  • end‑to‑end AI/ML solutions
  • technical architecture for AI-powered features and platforms
  • elevate engineering quality
  • Continuously improve AI system performance, reliability, and latency
  • ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows)
  • operating production AI systems at scale
  • ML services
  • NLP, deep learning, or document understanding applications
  • highly regulated systems
  • leading technical teams or initiatives
  • ML Ops, including model deployment, monitoring, and lifecycle management