About the role

Software Engineering Intern developing AI-enabled tools to improve productivity at Provation. Collaborating with senior engineers and contributing to automation and AI integration initiatives.

Responsibilities

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.
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.
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.
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).

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.
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).

Collaborate with engineers and partners to understand requirements and deliver working solutions
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.