Senior AI Engineer

Cornell University•Ithaca, NY

1d•Onsite

About The Position

The Senior AI Engineer serves as the technical lead for Cornell’s AI Engineering team, providing expert guidance, architectural leadership, and hands-on development for enterprise AI services. The role designs, develops, and maintains complex, production-grade AI applications and systems, with an emphasis on security, scalability, and reliability. It mentors developers and technical leads, establishes engineering best practices, and guides AI projects throughout the development lifecycle. The position directs and evaluates advanced AI solutions, leads the development and improvement of enterprise AI systems, and formulates standards and methodologies for building AI applications across the university. In collaboration with the Principal Solutions Architect for the AI Platform, the engineer defines patterns and reference architectures so AI Innovation Lab applications are production-ready and aligned with Cornell’s secure AI infrastructure. The role applies expertise in Generative AI, API development, authentication and identity protocols, and cloud-native architectures, and integrates AI Innovation Lab services with Cornell enterprise systems using standards such as OAuth 2.0, OIDC, SAML, JWT, LDAP, and Microsoft identity services. The Senior AI Engineer develops or refines procedures for complex challenges involving system capacity, operational constraints, and distributed AI system performance. As a senior leader in the AI Innovation Lab, the role provides technical direction to project teams that include project managers, business analysts, subject-matter experts, developers, and quality-assurance specialists. The engineer helps ensure solutions are well designed and implemented and align with institutional strategy. The position is 100% onsite on Cornell University’s campus in Ithaca, New York. Visa sponsorship and relocation assistance are not available. Cornell describes its benefits as including comprehensive health care options, generous retirement contributions, wellness programs, employee discounts, leave provisions, and educational benefits.

Requirements

Bachelor’s degree in computer science, software engineering, information technology, or a related field, or an equivalent combination of education and experience.
5–7 years of professional software engineering experience, with progressively advanced responsibility designing and developing production-grade applications and services.
Proven ability to design, build, test, deploy, and maintain complex AI- or ML-driven production applications with strong security, reliability, and scalability.
Strong experience developing cloud-native applications and services, preferably on AWS or a comparable cloud platform.
Demonstrated expertise in Generative AI technologies, API development, and distributed systems engineering.
Experience integrating applications with enterprise identity and authentication systems using protocols or services such as OAuth 2.0, OIDC, SAML, JWT, LDAP, and Microsoft identity services.
Demonstrated ability to provide technical leadership, mentor developers and technical leads, establish engineering best practices, and guide solutions throughout the development lifecycle.
Strong analytical and problem-solving skills, including experience addressing system capacity, performance, and operational challenges.
Excellent written and verbal communication skills, including the ability to explain complex technical concepts to diverse audiences.
Ability to cultivate and develop inclusive working relationships with students, faculty, staff, and community members.
A resume is required for further consideration.
No visa sponsorship is available for this position.

Nice To Haves

Master’s degree in computer science, data science, software engineering, information systems, or a related technical field.
Experience in higher education, research computing, or similarly complex enterprise environments, especially supporting faculty or research-driven AI use cases.
Hands-on experience delivering and operating production Generative AI or LLM-backed applications, including model orchestration, vector databases, and AI agent frameworks.
Experience with containerization and orchestration technologies such as Docker, Kubernetes, or ECS, and building scalable distributed systems.
Familiarity with DevOps and MLOps practices, including CI/CD pipelines, observability, monitoring, logging, metrics, tracing, and reliability-focused engineering for customer-facing services.
Experience participating in incident response and on-call operations for production systems.
Experience designing or supporting secure, production-grade AI solutions hosted on AWS, Azure, or GCP.
Knowledge of model governance, responsible AI frameworks, and AI risk management, including data privacy and compliance considerations.
Experience with enterprise integration patterns, secure API gateways, and identity-driven architectures.
Demonstrated ability to mentor engineers, lead design reviews, define technical standards, and influence architectural decisions across multiple teams.
Experience contributing to or supporting grant-funded research initiatives, innovation projects, or cross-institution collaborations.
A cover letter outlining interest and how experience and goals align with the role is strongly encouraged.

Responsibilities

Provide technical leadership, architectural guidance, and hands-on development for the AI Engineering team and Cornell enterprise AI services.
Design, develop, and maintain complex production-grade AI applications and systems that meet security, scalability, and reliability standards.
Mentor developers and technical leads, establish engineering best practices, and guide solutions through the development lifecycle.
Direct and evaluate advanced AI solutions and lead the development and enhancement of enterprise AI systems.
Formulate standards and methodologies for building AI applications across the university.
Collaborate with the Principal Solutions Architect for the AI Platform to define patterns and reference architectures for production-ready AI Innovation Lab applications aligned with Cornell’s secure AI infrastructure.
Integrate AI Innovation Lab services with Cornell enterprise systems using identity and authentication standards and services.
Develop or refine procedures to address complex system capacity, operational, and distributed AI performance challenges.
Provide technical direction to multidisciplinary project teams and help ensure solutions align with institutional strategy.
Foster an inclusive, respectful, cooperative, and healthy work environment and support work/life integration.