Senior Applied AI Engineer

Role Overview

• 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

Requirements

• US Citizen or authorized to work in 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

Tech Stack

• Assembly
• AWS
• Cloud
• Distributed Systems
• NoSQL
• Python
• SQL

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development