Sr. SysDev Eng, OTS - Data ANCHOR Team

Job summary

DESCRIPTION Are you a builder who thrives in ambiguity? Do you get energized turning proof-of-concepts into production-grade AI systems, fast and with a high bar? If you want to design agentic solutions that solve real operational problems at Amazon's global scale, this role is for you.

Benefits

Qualifications

• Experience leading the design, automation, deployment, and support of large-scale infrastructure
• Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
• 3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience
• Experience with designing and building application using AWS services such as Lambda, AWS Elastic Beanstalk, Kubernetes
• PREFERRED
• Experience in Kafka, or experience in software development and experience in any Bigdata architecture
• Experience integrating with third-party APIs and enterprise platforms (e.g., ITSM
• and location.
• Amazon also offers comprehensive

Responsibilities

• You will design, build, and ship technical solutions, agentic AI automations and
• Development & AI Solutions
• Design and build production-grade agentic AI solutions — from agent orchestration to API integrations with ServiceNow (3P) and Amazon internal systems (1P)
• Develop MCPs, agentic frameworks, and automation systems that enable scalable, reusable architectures across the Decision Intelligence portfolio
• Partner with Data Scientists and Data Engineers to productionize ML models, build AI-ready data pipelines, and develop agent capabilities (LLM orchestration, prompt engineering, RAG, tool-use patterns)
• Turn POCs into real, scalable production systems with a high quality bar — rapidly prototype, validate, and harden solutions for long-term deployment
• Architecture & Integration
• Architect end-to-end systems that connect AI agents to enterprise workflows, enabling automated ticket processing, device management, and operational decision-making at scale
• Build integrations across OTS and RME ecosystems, connecting agents to multiple upstream/downstream services, data sources, and streaming platforms
• Design user-facing platforms and dashboards where AI integrations surface prescriptive recommendations to field operations partners
• Design guardrails, evaluation frameworks, and safety mechanisms to ensure production AI systems operate reliably at scale
• Engineering Excellence & Operational Ownership

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