Senior Applied AI Engineer

Responsibilities Design, build, and maintain production-grade AI systems and customer-facing AI features Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services Build backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows Design and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly Integrate foundation models through platforms such as Amazon Bedrock or Agent Core Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability Mentor and collaborate with engineers across both software and AI disciplines Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability Qualifications US Citizen or authorized to work in the US 5+ years of professional software engineering experience building production systems Strong proficiency in Python Strong backend engineering fundamentals and experience building scalable APIs, services, distributed systems, or workflow orchestration platforms Proven hands-on experience building and shipping AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies in production environments Experience designing and implementing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or retrieval-augmented generation architectures Strong understanding of the practical challenges involved in production AI systems, including hallucination mitigation, evaluation, reliability, observability, latency, and cost management Experience building production software systems with strong engineering standards around testing, QA, deployment, monitoring, and maintainability Strong understanding of modern software engineering practices, including Git workflows, code review, CI/CD, automated testing, operational debugging, and release management Experience working with cloud infrastructure, preferably AWS Experience working with SQL and/or NoSQL databases Strong debugging, systems-thinking, and problem-solving skills Ability to operate effectively in fast-moving environments with evolving requirements and imperfect information Strong communication skills and ability to collaborate across technical and non-technical teams #J-18808-Ljbffr