AI Infrastructure / Product Engineer

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an AI Infrastructure / Product Engineer based in the United States.

We are seeking an AI Infrastructure / Product Engineer to bridge the gap between AI research and production-ready product development in the health technology sector. In this role, you will transform experimental AI capabilities into reliable, scalable, and user-focused systems that deliver measurable value. You will build evaluation frameworks and benchmarking pipelines to assess model accuracy, retrieval quality, hallucinations, latency, and cost. Working across backend engineering, cloud infrastructure, and AI development, you will help ensure that intelligent systems perform consistently in real-world environments. You will collaborate closely with AI researchers and product-focused colleagues to turn promising ideas into dependable features and workflows. This is an opportunity to contribute to the development of innovative AI products while shaping how their quality, performance, and reliability are measured and improved.

Accountabilities:

• Design and implement repeatable benchmarks and evaluation pipelines to measure AI system performance, including model accuracy, hallucinations, retrieval quality, latency, and operational costs.

• Develop dashboards and monitoring tools that provide clear visibility into system performance, quality trends, and the impact of engineering improvements.

• Transform research prototypes into production-ready retrieval, drafting, document-processing, and agent-based workflows.

• Build and maintain backend services, APIs, and internal tools that support experimentation, testing, evaluation, and the release of product improvements.

• Deploy and operate cloud infrastructure, including databases, monitoring systems, delivery pipelines, and production reliability processes.

• Establish processes for continuously testing, measuring, and improving AI system quality and performance.

• Collaborate with AI researchers to translate experimental findings into practical product capabilities while maintaining a strong focus on user needs.

• Turn ambiguous technical challenges and evolving research concepts into maintainable, reliable, and scalable software solutions.

Requirements:

• At least 3 years of professional experience as a software engineer, infrastructure engineer, or product engineer building and deploying production systems.

• Strong proficiency in Python and practical experience deploying applications and services in cloud environments.

• Experience developing APIs, backend services, database integrations, deployment pipelines, and production monitoring systems.

• Hands-on experience building evaluation pipelines, benchmarks, or assessment frameworks for machine learning or AI-powered systems.

• Ability to translate ambiguous requirements, experimental code, and emerging ideas into usable, maintainable production products.

• Interest in or experience with large language models (LLMs), retrieval-augmented generation (RAG), AI agents, model benchmarking, or document automation.

• Strong problem-solving skills and the ability to work effectively across AI research, infrastructure, and product development.

• Full-stack development experience is an advantage.

• Familiarity with technologies such as FastAPI, Docker, React, Next.js, PostgreSQL, and vector databases is a plus.

• Ability to work remotely from within the United States.

Benefits:

• Annual base salary: $180,000–$260,000 USD, depending on skills, experience, and role scope.

• Full-time, remote work arrangement within the United States.

• Opportunity to work at the intersection of AI research, infrastructure engineering, and product development.

• The chance to build and improve production AI systems with measurable impact on product quality and reliability.

• Opportunities to work with modern AI technologies, evaluation frameworks, and scalable cloud infrastructure.