Principal AI Platform Engineer

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.

JOB SUMMARY

We are seeking a Principal AI Platform Engineer to provide hands-on technical leadership for the design, development, deployment, and operation of foundational AI capabilities powering Claritev’s next generation of healthcare products.

This role is for an experienced platform engineer with a proven track record of turning AI innovation into reliable, secure, scalable, observable, and governed production services. You will architect and deliver reusable AI capabilities, including document ingestion, RAG and knowledge retrieval, entity extraction, model/LLM services, and agentic workflows, through production, grade micro-services, APIs, and platform components.

You will partner closely with Product, Engineering, AI Science, and business leaders to translate business needs into production, ready platform capabilities. You will establish engineering and operational standards across reliability, evaluation, observability, security, governance, and cloud architecture, while helping shape Claritev’s AI Foundry and technical strategy.

JOB ROLES AND RESPONSIBILITIES

• Own the engineering, deployment, operation, and continuous improvement of production AI platform services, including scalable microservices, APIs, SDKs, and shared components used across teams.
• Partner with Product, Engineering, AI Science, and business stakeholders to define platform requirements, technical approaches, success metrics, and delivery plans.
• Drive end-to-end production readiness across AI services, including cloud deployment, enterprise integration, scalability, resilience, fault tolerance, performance, monitoring, incident response, failure recovery, and ongoing operational support.
• Establish reusable platform frameworks, APIs, data contracts, code components, and engineering patterns that enable product and engineering teams to adopt AI capabilities efficiently and consistently.
• Implement and enforce engineering standards for CI/CD, automated testing, versioning, controlled deployment, rollback, SLOs/SLIs, operational readiness, and production troubleshooting.
• Build observability and monitoring into the platform, including end-to-end tracing, logging, metrics, alerting, and monitoring of AI quality, latency, reliability, and cost.
• Establish automated evaluation, benchmarking, regression testing, and production quality gates for foundational AI capabilities such as document parsing, RAG, entity extraction, and agents.
• Embed security and governance into production services, including authentication and authorization, PHI/PII protection, lineage, provenance, auditability, policy enforcement, reproducibility, and applicable HIPAA/data-governance requirements.
• Build reliable RAG and knowledge-retrieval capabilities using embeddings, vector and hybrid search, structured data, knowledge graphs, and enterprise knowledge sources.
• Provide hands-on technical leadership across teams, partner with Product, Engineering, AI Science, and business stakeholders on platform requirements, and influence engineering and architecture decisions while mentoring engineers and promoting production excellence.

JOB REQUIREMENTS

Education

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

Experience

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

Technical Skills

Production AI & Cloud Engineering

Proven track record designing, shipping, and operating production-grade AI systems, with expert-level Python and experience building scalable microservices, APIs, SDKs, and distributed systems on OCI, Azure, AWS, or equivalent cloud platforms.

AI Foundry & Platform Architecture

Experience building reusable foundational AI capabilities, including document ingestion, RAG, entity extraction, model/LLM services, and agentic workflows, for consumption across multiple engineering and product teams.

Reliability & Production Operations

Strong experience engineering resilient production systems, including scalability, fault tolerance, retries, idempotency, caching, rate limiting, SLOs/SLIs, incident management, and disaster recovery.

Observability & Monitoring

Experience implementing end-to-end tracing, logging, metrics, alerting, and AI quality, latency, and cost monitoring across models, retrieval, agents, services, and infrastructure.

AI Evaluation & Quality Engineering

Experience designing and operationalizing automated evaluation and benchmarking frameworks for foundational AI capabilities such as document parsing, RAG, entity extraction, and agents, including gold datasets, quality metrics, regression testing, production monitoring, and release quality gates.

Security, Governance & Auditability

Experience embedding authentication/authorization, sensitive-data protection, lineage, provenance, auditability, policy enforcement, and reproducibility into production AI platforms.

DevOps / LLMOps

Strong experience with CI/CD, automated testing, infrastructure as code, environment management, versioning, controlled deployments, rollback, and production troubleshooting for AI systems.

RAG, Data & AI Infrastructure

Hands-on experience with document processing, embeddings, vector/hybrid search, entity extraction, knowledge graphs, structured outputs, and agent orchestration, and the infrastructure required to productionize them.

Other Skills

• Strong problem-solving, critical-thinking, communication, and organizational skills.
• Ability to communicate complex technical concepts clearly to technical and non-technical stakeholders.
• Ability to operate effectively in a fast-moving, cross-functional environment.

Preferred Qualifications

• Experience in healthcare, health technology, insurance, claims, payment integrity, or other regulated industries.
• Experience building AI systems that process sensitive data, including PHI or PII.
• Experience with process automation and integration across enterprise workflows and systems.

COMPENSATION

The base salary range for this position is $190K to $210K. Specific compensation offers are determined based on a variety of factors including the candidate’s education, experience, skills, work location, and internal equity considerations. In addition to base salary, this position is eligible for an annual performance bonus and a comprehensive benefits package, including health insurance and a 401(k) retirement plan.

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