Overview of Role

We are seeking a Senior AI Engineer who is passionate about building production-ready AI applications using Generative AI, Agentic AI, and modern software engineering practices. This role is focused on designing, developing, and deploying AI solutions that solve meaningful business and clinical problems.

The ideal candidate has hands-on experience building and delivering production AI solutions for enterprise users and enjoys translating business requirements into scalable, reliable AI applications. They bring strong software engineering fundamentals, applied AI expertise, and a collaborative mindset to deliver solutions from concept through production.

Responsibilities:

• Design, build, and maintain production-grade Generative AI, Agentic AI, and machine learning applications.
• Develop evaluation and testing approaches to measure model performance, identify regressions, and improve solution quality.
• Apply LLMs, multimodal models, retrieval-augmented generation (RAG), structured extraction, and tool calling to solve real-world healthcare workflows.
• Partner with Engineering, Product, Data, and Operations teams to integrate AI capabilities into client’s systems.
• Improve the reliability, observability, guardrails, and monitoring of production AI applications.
• Stay current with advancements in AI technologies and apply them pragmatically to deliver business value.

Qualifications:

• Bachelor’s degree – Required
• Master’s degree – Preferred
• 5+ years of software engineering, applied AI, machine learning, or related technical experience, including at least 1 year of recent experience designing and delivering production-grade Generative AI solutions.
• Hands-on experience with modern AI systems, including LLMs, retrieval-augmented
• generation (RAG), agents, structured extraction, classification, or workflow automation.
• Proficiency in Python, with experience building cloud-native applications on AWS or GCP and working with relational and/or NoSQL databases.
• Practical experience with prompt engineering, tool calling, retrieval, model selection, context management, and AI application debugging.
• Experience evaluating AI system quality using metrics, test datasets, human review, and error analysis.
• Strong software engineering fundamentals, including system design, testing, scalability, and maintainability.
• Hands-on experience building retrieval-augmented AI applications using vector databases, embeddings, and semantic search.
• Preferred: Experience working in healthcare, insurance, financial services, or another regulated industry.
• Preferred: Familiarity with AI evaluation, prompt optimization, guardrails, or human-in-the-loop workflows.