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Remote role, strong employer brand, and general SDE title but specialized LLM requirements yield medium competition.
Heavy emphasis on production LLM, agentic AI, and ML governance creates high domain specificity.
Mandatory 10+ years, required production LLM experience, cloud/Kubernetes and multi-stack skills enforce high selectivity.
Lead design and architecture of complex, distributed cloud-native AI systems for product classification and compliance workflows.
Develop and operate agentic AI workflows integrating LLM-based reasoning, data retrieval, and human-in-the-loop escalation with measurable accuracy improvements.
Own end-to-end full-stack engineering and platform operations including APIs, asynchronous pipelines, Kubernetes deployment, observability, incident management, and security best practices.
Bachelor's degree in Computer Science.
Minimum 10 years of professional software engineering experience with senior-level technical ownership.
Proficiency in Java (Spring Boot) or Python (FastAPI/asyncio) and experience deploying services on AWS with Docker/Kubernetes.
Hands-on experience integrating LLMs or ML models into production systems with capabilities including prompt design, output validation, and responsible AI practices.
Experienced in designing scalable, resilient distributed systems using event-driven and domain-driven design principles with strong architectural decision-making.
Demonstrated track record of delivering production-grade agentic AI capabilities that materially improve product outcomes and engineering velocity.
Capable mentor and technical leader who improves code quality, engineering standards, and team AI adoption through coaching and collaborative practices.