Principal Applied AI Engineer, Agentic AI

At Claritev, our mission is to simplify healthcare workflows, improve transparency, and bend the healthcare cost curve. We believe that data, technology, and AI can fundamentally transform how healthcare operates by automating complex workflows, improving decision‑making, and reducing unnecessary costs across the system.

By combining deep healthcare expertise with advanced analytics and AI, we help payers, providers, and employers operate more efficiently and deliver better outcomes for the people they serve.

We are bold in our thinking, rigorous in execution, and committed to service excellence for every stakeholder. Our culture values innovation, accountability, diversity of thought, and collaboration.

Join us as we accelerate our transformation into a leading technology and AI‑driven company shaping the future of healthcare.

• We are seeking a Principal AI Engineer to provide hands‑on technical leadership for the design, development, and production deployment of advanced AI systems powering Claritev's next generation of healthcare products.
• This role is for an experienced engineer who excels at turning AI innovation into reliable, secure, scalable, and measurable production capabilities. You will architect and deliver predictive, generative, and agentic AI systems that automate complex healthcare workflows and unlock insights from large‑scale healthcare data.
• You will partner closely with Product, Engineering, AI Science, and business leaders to translate business needs into production‑ready AI solutions. You will also establish engineering standards, make key architectural decisions, mentor engineers and scientists, and help shape Claritev's AI platform and technical strategy.

• Lead the architecture, development, deployment, and operation of production AI applications, services, and platforms.
• Design and implement agentic AI systems, including tool integration, orchestration, memory, retrieval, workflow execution, and human‑in‑the‑loop controls.
• Build reliable RAG and knowledge‑retrieval capabilities using embeddings, vector databases, structured data, and enterprise knowledge sources.
• Establish reusable frameworks, APIs, code components, and engineering patterns that enable teams to build and deploy AI solutions efficiently and consistently.
• Partner with Product and business stakeholders to identify high‑value AI opportunities and define technical approaches, success metrics, and delivery plans.
• Drive end‑to‑end delivery from prototype through production, including integration with enterprise systems, monitoring, observability, evaluation, and ongoing improvement.
• Establish standards for AI/ML quality, including offline and online evaluation, reliability, latency, cost, safety, and model performance.
• Ensure secure and responsible use of AI, including privacy, PHI/PII protection, explainability, auditability, and compliance with HIPAA and applicable data‑governance requirements.
• Provide technical leadership across complex, cross‑functional initiatives; influence architecture and engineering decisions beyond an individual project.
• Mentor engineers and data scientists and promote a culture of technical excellence, continuous learning, and pragmatic innovation.

• Bachelor's degree in computer science, Engineering, Data Science, a quantitative discipline, or a related field required.
• Master's degree or PhD preferred.

• 10+ years of hands‑on experience in software engineering, machine learning engineering, applied AI, or a related technical discipline.
• 5+ years of experience designing and delivering production‑grade ML or AI systems.
• 3+ years of experience building with generative AI, LLMs, RAG, and/or agentic AI systems.
• Demonstrated experience leading complex technical initiatives from concept through production deployment and measurable business impact.

• Strong software engineering skills, including expert‑level Python proficiency and experience designing scalable services, APIs, and distributed systems.
• Strong foundation in machine learning, deep learning, statistics, optimization, and model lifecycle management.
• Experience…