AI Solutions Engineer

AI Solutions Engineer

Reporting to the Sr. Director of AI & Emerging Technologies, the AI Solutions Engineer will serve as a technical lead for designing and implementing AI-enabled solutions that address business and operational needs across Columbia University. Working closely with stakeholders, the AI Generalist, and CUIT technical partners, this role will translate requirements into scalable technical solutions using programming, machine learning, large language models, prompt engineering, data engineering, automation technologies, APIs, and enterprise AI platforms.

The AI Solutions Engineer will support proof-of-concept development, architecture and tool selection, optimization of AI capabilities, and the responsible deployment of solutions aligned with University standards.

The Emerging Technologies team is a fast-paced, startup inspired group that develops extremely innovative solutions to some of the most challenging problems in higher education and research.

The ideal candidate will have the following skillset:

• Technical AI Solutions Lead
  - You can design practical AI-enabled solutions that balance user needs, technical feasibility, security, scalability, and supportability.
• Hands-On Builder
  - You are comfortable developing prototypes, integrations, automations, data workflows, prompts, and proof-of-concepts using modern AI and software tools.
• Consultative Engineer
  - You can partner with non-technical stakeholders to clarify requirements and explain tradeoffs in accessible language
• Enterprise-Minded Architect
  - You understand that successful AI solutions require governance, privacy, accessibility, reliability, documentation, and operational handoff.
• Continuous Learner
  - You stay current with rapidly evolving AI platforms, model capabilities, development patterns, and responsible AI practices.

The successful candidate will be a pragmatic, hands-on engineer who can move from ambiguity to working solutions while partnering effectively across CUIT and ensuring AI capabilities are implemented responsibly, securely, and at enterprise scale.

Responsibilities

• AI Solution Design:
  Translates business and operational requirements into technical designs, solution options, implementation plans, and recommendations for AI-enabled services.
• Proof-of-Concept Development:
  Build and evaluate prototypes, pilots, automations, integrations, and proof-of-concepts using large language models, APIs, enterprise AI platforms, and related technologies.
• Prompt Engineering & Model Optimization:
  Designs, tests and refines prompts, workflows, retrieval patterns and model configurations to improve solution quality, usability, and reliability.
• Data & Integration Engineering:
  Develops and/or coordinates data flows, API integrations, connectors, and automation patterns required to support AI-enabled use cases.
• Architecture & Tool Selection:
  Advises on platform capabilities, vendor tools, build-versus-buy considerations, technical constraints, and scalable implementation approaches.
• Responsible AI & Governance Alignment:
  Partners with Security/Risk, Enterprise Architecture, data owners, accessibility partners, and other stakeholders to ensure solutions align with University policies and standards.
• Technical Documentation & Handoff:
  Creates technical documentation, implementation notes, support handoff materials, and reusable patterns to enable operational support and future reuse.
• Stakeholder
  
  Collaboration:
  
  Works closely with the AI Generalist, Emerging Technologies team members, faculty, staff, business units, vendors, and CUIT partners to deliver high-value solutions.
• Continuous Improvement:
  Identifies reusable components, accelerators, automation patterns, evaluation methods, and technical standards that improve the maturity of AIaaS delivery.
• All other duties as assigned.

Minimum Qualifications

• Bachelor's degree and/or its equivalent required.
• Minimum 4-6 years' related experience.
• 4-6 years of progressively responsible experience in software engineering, solutions engineering, systems integration, data engineering, automation, machine learning, or related technical roles.
• Hands-on experience with programming or scripting languages such as Python, JavaScript, Type Script, PHP, or similar languages.
• Experience building solutions with APIs, automation platforms, data pipelines, integrations, or cloud/enterprise platforms.
• Working knowledge of large language models, prompt engineering, AI application patterns, machine learning concepts, and responsible AI considerations.
• Demonstrated ability to translate business requirements into technical designs, prototypes, and production-ready or supportable solution approaches.
• Strong analytical, troubleshooting, documentation, and communication skills, including the ability to explain technical tradeoffs to non-technical stakeholders.
• Ability to manage multiple concurrent initiatives in a fast-paced, deadline-driven environment with changing priorities.
• Ability to work with minimal supervision and exercise sound judgment when…