Lead Applied AI Architect

The role combines enterprise architecture accountability with hands-on engineering. The architect directs the technical work of Applied AI Engineers, serves as technical reviewer for vendor and third-party AI, and moves AI capabilities from prototype to governed production in partnership with data science, informatics, cybersecurity, and clinical leaders.

Essential Functions

• Lead the design, development, and optimization of AI agent architectures tailored for innovative and scalable solutions.
• Drive full-cycle machine learning model deployment, including data preprocessing, training, testing, and integration into production systems.
• Oversee the development and implementation of algorithms for natural language understanding (NLU), decision-making, and contextual reasoning.
• Collaborate with cross-functional teams to ensure seamless integration between AI platforms and existing enterprise systems.
• Mentor and guide a team of engineers and data scientists to enhance their technical skills and operational efficiency.
• Lead efforts in continuous improvement, including model retraining, A/B testing, and performance evaluation.
• Stay ahead of emerging trends in AI and machine learning to adapt and innovate in alignment with business needs.
• Establish and enforce best practices in code quality, documentation, and version control within AI projects.
• Conduct regular reviews and present findings or developments to stakeholders and senior leadership.
• Manage resources and prioritize tasks to meet project deadlines and alignment with strategic objectives.
• Ensure compliance with ethical AI practices and applicable legal standards in all development and deployment activities.
• Identify opportunities for leveraging AI to solve complex business challenges and drive operational efficiencies.

Education Qualifications

Bachelor's Degree in Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or related field.

Licenses and Certifications

At least one relevant cloud, machine learning, or software engineering certification.

Experience Qualifications

Ten (10) years of professional engineering experience, including at least four (4) years designing and building AI/ML systems and at least two (2) years architecting, deploying, and operating LLM-based or agentic applications in production. Prior experience as a technical lead or architect with design authority across multiple teams, including code review and mentorship, required. Enterprise data/development platform (e.g. Palantir Foundry or similar) experience.

Skills and Abilities

• Architecture and platform leadership - ability to define enterprise reference architectures, reusable patterns, engineering standards, technical roadmaps, and build-versus-buy recommendations.
• Software and AI engineering - advanced proficiency in Python, SQL, APIs, distributed application design, automated testing, version control, and production AI application development.
• Responsible and secure AI - deep knowledge of evaluation, guardrails, privacy, security, threat modeling, access control, auditability, and human-in-the-loop design.
• Technical leadership and judgment - ability to make and defend enterprise architectural decisions, explain tradeoffs to executive and clinical leaders, direct and mentor engineers, and balance innovation, safety, value, and delivery.
• Production operations - experience with CI/CD, MLOps/LLMOps, observability, incident response, rollback, reliability, lifecycle management, and cost optimization.
• Workflow orchestration and automation - ability to design end-to-end automated workflows spanning enterprise applications, APIs, document repositories, and human approval steps.
• Collaboration: communicates effectively across technical and clinical audiences.
• Healthcare integration - working knowledge of clinical workflows, PHI handling, APIs, enterprise data platforms, and healthcare interoperability standards such as HL7/FHIR.