Software Engineering Intern – AI Enablement

The Software Engineering Intern – AI Enablement supports the design and delivery of AI-enabled tools and
automation that improve internal productivity and adoption of AI capabilities. The intern will work under the
guidance of senior engineers to help build a single view of AI adoption and ROI across tools, improve the
usability and installation experience of internal AI developer resources, and automate repeatable AI-related
tasks into reliable scheduled jobs. The role partners with technical and business stakeholders to deliver secure,
scalable solutions.

Key Responsibilities:
1. Foundational Programming Skills
• Assist with building and enhancing internal software tools that support AI enablement initiatives, under
senior guidance.
• Write, test, and debug code for well-defined features and automation tasks.
• Apply object-oriented programming fundamentals and software design patterns as part of day-to-day
development work.
2. Cloud & SaaS Awareness
• Support cloud-based development activities related to internal AI tooling and reporting (e.g., services,
scripts, or integrations).
• Gain exposure to CI/CD and basic deployment concepts for internal tools (build, release, versioning,
environment awareness).
• Contribute to automation work such as migrating manual tasks into scheduled jobs where applicable.
3. Healthcare Compliance Knowledge
• Follow secure coding practices and team standards, including responsible handling of data and access as
required by the team’s environment.
• Build awareness of healthcare privacy concepts and interoperability standards as relevant to the
systems and workflows your tooling may touch.
4. AI Readiness & Data Literacy
• Contribute to initiatives that track and improve AI usage, including helping build a single view of AI
adoption and ROI across tools.
• Support the improvement of internal AI enablement assets (e.g., expanding an AI developer repository
into a more turnkey installation/adoption experience).
• Develop familiarity with AI/ML concepts and how AI-enabled solutions are embedded into real
workflows (prototyping and scaling concepts as exposure allows).
5. Collaboration & Communication
• Collaborate with engineers and partners to understand requirements and deliver working solutions
incrementally.
• Participate in agile team routines (standups, planning, demos) and communicate progress, risks, and
learnings.
• Contributes to documentation and enablement artifacts (how-to notes, setup steps, basic runbooks) to
help others adopt and sustain solutions.

Required Qualifications:

Education & Experience
• Currently pursuing a Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a
related field. Graduating December 2026 or later. 
• Internship, academic, or project experience in software development (coursework, capstone, research,
or personal projects).
• Interest in AI enablement, automation, developer tooling, or analytics/measurement (adoption, usage,
ROI) is strongly preferred.

Other Preferred Knowledge, Skills, Abilities or Certifications:
• Programming Languages: Python, JavaScript, or Java (TypeScript a plus).
• Cloud Platform Exposure: Familiarity with AWS or Azure; exposure via labs/projects acceptable.
• Version Control & Collaboration Tools: Git/GitHub; familiarity with Jira or similar tools.
• Automation & Scheduling Concepts: Comfort building repeatable scripts or automations; interest in
turning manual tasks into scheduled jobs.
• Documentation/Enablement Mindset: Ability to write clear setup steps or lightweight guides that help
others adopt tools.
• AI Awareness: Exposure to AI/ML fundamentals and/or interest in practical LLM/automation use cases
(not required, but helpful).