Description
- 7+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- 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.
- 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.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
We are seeking a highly technical Senior Process Engineer to lead AI-driven engineering initiatives in the Compliance domain. This senior role focuses on designing, building, and operating AI-powered solutions that improve Seller compliance outcomes and significantly enhance operational efficiency.
The ideal candidate brings deep hands‑on expertise in AI/ML development, with a strong track record of building, deploying, and scaling production-grade AI systems. You will design system architectures, build efficient ML models and data pipelines, and drive technical collaboration across engineering, science, and product teams.
You are passionate about writing high‑quality code, making sound architectural decisions, and ensuring that AI systems are robust, maintainable, and secure. You excel at translating business requirements into scalable solutions, mentoring engineers, and driving technical excellence in a fast‑paced environment. Experience with Python, cloud platforms (e.g., AWS), and ML operations is essential; familiarity with compliance or regulatory domains is preferred. This role offers the opportunity to shape the future of Compliance via innovative AI engineering solutions.
Key job responsibilities
Technical Development & Architecture
* Design and implement scalable AI/ML solutions for Compliance use cases
* Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving.
* Build robust, production-grade AI services using Python and modern ML frameworks.
* Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective.
* Establish and maintain high engineering standards, including testing, monitoring, and documentation.
Engineering Leadership
* Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions.
* Define and evolve the technical architecture for AI-powered features and platforms.
* Lead code reviews, enforce best practices, and elevate engineering quality across the team.
* Continuously improve AI system performance, reliability, and latency through experimentation and optimization.
Technical Collaboration & Operations
* Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work.
* Provide technical guidance and mentorship to junior and mid‑level engineers.
* Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems.
* Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- 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.
- 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.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
We are seeking a highly technical Senior Process Engineer to lead AI-driven engineering initiatives in the Compliance domain. This senior role focuses on designing, building, and operating AI-powered solutions that improve Seller compliance outcomes and significantly enhance operational efficiency.
The ideal candidate brings deep hands‑on expertise in AI/ML development, with a strong track record of building, deploying, and scaling production-grade AI systems. You will design system architectures, build efficient ML models and data pipelines, and drive technical collaboration across engineering, science, and product teams.
You are passionate about writing high‑quality code, making sound architectural decisions, and ensuring that AI systems are robust, maintainable, and secure. You excel at translating business requirements into scalable solutions, mentoring engineers, and driving technical excellence in a fast‑paced environment. Experience with Python, cloud platforms (e.g., AWS), and ML operations is essential; familiarity with compliance or regulatory domains is preferred. This role offers the opportunity to shape the future of Compliance via innovative AI engineering solutions.
Key job responsibilities
Technical Development & Architecture
* Design and implement scalable AI/ML solutions for Compliance use cases
* Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving.
* Build robust, production-grade AI services using Python and modern ML frameworks.
* Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective.
* Establish and maintain high engineering standards, including testing, monitoring, and documentation.
Engineering Leadership
* Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions.
* Define and evolve the technical architecture for AI-powered features and platforms.
* Lead code reviews, enforce best practices, and elevate engineering quality across the team.
* Continuously improve AI system performance, reliability, and latency through experimentation and optimization.
Technical Collaboration & Operations
* Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work.
* Provide technical guidance and mentorship to junior and mid‑level engineers.
* Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems.
* Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).
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