Job overview
- Location
- Bengaluru, Karnataka, India
- Job type
- Full-time
- Work mode
- On-site
- Salary
- Not disclosed
- Category
- Engineering
- Posted
- Valid through
At a glance by CareerCola
- Company
- Amazon
- Role
- Software Development Engineer, AI Trust & Innovation
- Location
- Bengaluru, Karnataka, India
- Employment type
- Full-time
- Work mode
- On-site
- Category
- Engineering
- Posted
- 23 Sep 2026
- Valid through
- 23 Oct 2026
About the role
Amazon | Location: Bengaluru, Karnataka, IND | Employment type: FULL_TIME | Posted on CareerCola: 2026-09-23
Official listing: https://www.amazon.jobs/en/jobs/10557199/software-development-engineer-ai-trust-innovation
Description
DESCRIPTION
About the Team
Amazon's Devices & Services Trust CX Innovations team is building the future of responsible AI for consumer devices. We deliver privacy-first, accessible, and trustworthy AI experiences that millions of customers use daily across Amazon's device ecosystem—including Alexa, Echo, and other ambient computing products. Our mission is to ensure that as we push the boundaries of generative AI innovation, we maintain Amazon's high bar for customer trust, privacy, inclusion, and accessibility.
Role Overview
We are seeking an exceptional Software Development Engineer II to build the foundational systems and consumer-facing features that enable trustworthy AI experiences at scale. You will partner closely with our Principal Engineers and Managers to design and implement privacy-preserving AI architectures, responsible AI frameworks, and accessibility features that set the standard for consumer AI products.
This is a high-impact role where you'll tackle complex technical challenges at the intersection of AI innovation and customer trust—balancing performance with privacy, building explainable AI systems, and creating guardrails that protect customers while enabling delightful experiences.
Key job responsibilities
What You'll Build
Privacy-Preserving AI Systems:
* Design and implement federated learning and differential privacy techniques for on-device and hybrid AI architectures
* Build privacy-preserving data pipelines that minimize data collection while maintaining model quality
* Create consent management frameworks and data minimization systems that give customers control
* Architect on-device vs. cloud processing trade-offs that optimize for both privacy and performance
Consumer-Facing Trust Features:
* Develop explainable AI interfaces that help customers understand how AI makes decisions
* Build transparency controls that show customers what data is used and how
* Create privacy dashboards and settings that give customers meaningful control over their AI experiences
* Implement cross-device identity management with privacy-first design principles
Key Technical Challenges
* Latency vs. Privacy Trade-offs: Optimize time-to-first-token and response times while maintaining strong privacy guarantees through on-device processing and selective cloud offloading
* AI Safety at Scale: Reduce hallucinations to
Skills
- AWS
- business development
- Software Engineering
- Trust & Safety
- Innovation
About Amazon
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