Job Description

Alexa Sensitive Content Intelligence team owns the Responsible AI platform for Alexa+ worldwide — building ML-powered infrastructure that keeps content safe across cloud and on-device experiences. We leverage traditional encoder models, RAG based retrieval and large language models to identify and moderate sensitive content in real-time across text, images, video, and LLM-generated responses.
As a Software Development Manager, you will lead an engineering team building production-grade ML inference infrastructure and content safety services spanning cloud-based real-time moderation and on-device moderation for Alexa+ enabled endpoints. You will own the end-to-end model lifecycle from onboarding to production serving, driving 1-click continuous deployment pipelines for encoder (BERT-class), decoder (Qwen 600M-class), and encoder-decoder models powering text classification, multimedia analysis, and LLM-based moderation workloads.
You will influence our overall strategy, drive system architecture decisions, grow engineers, establish best practices, and build relationships with partner ML science and product teams. You will regularly communicate with senior management on status, risks, and technical strategy.

Key job responsibilities
Lead and grow a high-performing engineering team delivering ML inference and content safety services for Alexa+ Responsible AI — covering both cloud and on-device moderation.
Own inference optimization — GPU/CPU serving strategies for cloud, and model compression/distillation strategies for on-device deployment on resource-constrained endpoints.
Lead building and operating 1-click continuous model deployment infrastructure supporting multiple model architectures (encoder, decoder, encoder-decoder) with zero-downtime rollouts, automated validation, and rollback — across cloud and edge targets.
Drive architecting scalable real-time and batch serving systems handling multi-modal content (text, image, video, LLM invocations) for worldwide Alexa+ moderation.
Drive on-device moderation capabilities — optimize models for latency, memory, and power constraints on Alexa+ devices while maintaining moderation accuracy.
Drive operational excellence — availability (99.95%+), latency, cost metrics, and multi-region reliability.
Partner with applied scientists on model architecture trade-offs and with platform teams on traffic migrations, capacity planning, and device integration.
Grow highly talented SDEs/MLEs with a thirst for learning emerging and future tech in the ML hosting and Infa optimisation space.

About the team
ASCI owns two charters - Alexa + RAI enablement and compliance and Alexa+ Customer feedback. The org has 60+ engineers, 30+ applied scientists, around 7 product managers and 50+ ops team. We work on exciting opportunities using LLMs/ encoder Models/ RAG based systems to enable Alexa + to be policy compliant world wide, across all regions and Locales. Our customer feedback org owns the Report A Bug (RAB) feature which serves as the voice of the customer. This particular team is the back bone of the entire org enabling efficient ML infrastructure for hosting 20+ ML models and interfacing with multiple LLMs in Bedrock.

Basic Qualifications

- 10+ years of developing large-scale, multi-tiered distributed software systems using distributed programming experience
- 3+ years of engineering team management experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience partnering with product or program management teams

Preferred Qualifications

- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers

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