Agentic AI Systems Intern at Medical Metrics Inc

Houston, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

25 May, 26

Salary

0.0

Posted On

24 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLMs, AI Assistants, Agentic AI Tools, REST APIs, Model Context Protocol, LLM Integrations, RAG Pipelines, Prompt Engineering, Workflow Automation, Python, JavaScript, Command-Line Environments, Git, Communication Skills, Self-Motivated, Proactive

Industry

Biotechnology Research

Description

Description This is a paid onsite internship. COMPANY INFORMATION: Medical Metrics Inc. (MMI) is a fast-growing, independent imaging core laboratory and medical imaging solutions company based in Houston, TX. MMI provides image analysis and consulting services to medical device, biologics, and pharmaceutical companies in support of their clinical trials and product R&D. Competitive hourly compensation commensurate with experience and qualifications. Hands-on exposure to frontier AI tools and real-world agentic systems in a growing healthcare technology company. JOB SUMMARY: The Agentic AI Systems Intern will support Medical Metrics, Inc. (MMI) in its initiative to adopt cutting-edge agentic AI tools across the organization. MMI operates an on-premises large language model (LLM) with custom tooling for retrieval-augmented generation (RAG) and text-based workflows, and is actively piloting cloud-based agentic platforms to enable employees to discover their own productivity boosters. The intern will play a hands-on role in rolling out these technologies to employees, developing and customizing LLM integrations using the Model Context Protocol (MCP), and helping to identify AI-driven solutions that improve efficiency, quality, and capability throughout MMI’s operations. This position offers an exceptional opportunity to gain real-world experience at the frontier of applied AI in a growing healthcare technology company. DUTIES AND RESPONSIBILITIES: Familiarize with MMI’s on-premises LLM, Claude Cowork, Claude Code, and associated custom tooling. Research risks and implement security solutions to enable responsible adoption of agentic AI. Support employees across the organization in setting up and effectively using agentic AI tools, including best practices for prompting, skill creation, and workflow automation. Explore and customize open-source plugins to address MMI-specific workflows and stakeholder needs. Develop MCPs and integrations to enable AI-driven data access and tool calls across MMI systems including shared network locations, databases, and SaaS platforms. Research and evaluate third-party AI tools to broaden MMI’s adoption options and inform strategy. Document workflows, configurations, and findings to support knowledge transfer and long-term sustainability of AI initiatives. Comply with corporate policies and procedures and applicable ISO standards. Requirements EDUCATION AND EXPERIENCE: Currently enrollment or recent graduate of a BS program in Computer Science, Information Systems, or a related field. SKILLS REQUIRED: Familiarity with LLMs, AI assistants, and agentic AI tools. Demonstrated expertise with REST APIs, Model Context Protocol (MCP), and LLM integrations. Prior exposure to RAG pipelines, prompt engineering, or workflow automation. Proficient with Python, JavaScript, or similar scripting languages. Strong enthusiasm for artificial intelligence, automation, and agentic AI systems. Comfort working in command-line environments and using version control tools such as Git. Ability to learn independently from open-source documentation, technical resources, and community forums. Strong written and verbal communication skills for working with both technical and non-technical stakeholders. Self-motivated with a proactive, entrepreneurial approach to problem-solving.

Responsibilities

The intern will support the adoption of agentic AI tools by rolling out technologies, developing and customizing LLM integrations using MCP, and identifying AI-driven solutions to improve operational efficiency. Duties include familiarizing with on-premises LLMs, researching security solutions, supporting employee adoption, exploring open-source plugins, and developing integrations for data access.