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Artificial Intelligence Engineer Intern Jobs in Indiana

United Parcel Service (UPS)

$15.25 - $19.75/hr

An Industrial Engineering Intern at UPS works on targeted projects individually, or in small teams ... Exploring and applying Artificial Intelligence (AI), automation, and data analytics tools to ...

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Artificial Intelligence Engineer Intern information

What does an artificial intelligence engineer intern do?

An Artificial Intelligence (AI) Engineer Intern assists in designing, developing, and testing AI models and algorithms under the guidance of experienced engineers. Their daily tasks may include data preprocessing, training machine learning models, evaluating model performance, and collaborating with team members on various AI projects. Interns often work with programming languages such as Python, and may utilize frameworks like TensorFlow or PyTorch. This role provides hands-on experience in applying AI techniques to real-world problems and helps interns build skills necessary for a career in artificial intelligence.

What types of projects and collaborations can an artificial intelligence engineer intern expect during their internship?

As an Artificial Intelligence Engineer Intern, you'll likely work on a variety of hands-on projects such as developing machine learning models, assisting with data preprocessing, and supporting the deployment of AI solutions. Interns often collaborate with cross-functional teams including data scientists, software engineers, and product managers to solve real-world business problems. You’ll gain exposure to production environments, code reviews, and may participate in team meetings or brainstorming sessions. This structure provides valuable experience and networking opportunities, while also allowing you to contribute meaningfully to ongoing projects.

What are the key skills and qualifications needed to thrive as an artificial intelligence engineer intern, and why are they important?

To thrive as an Artificial Intelligence Engineer Intern, you need a strong background in computer science fundamentals, programming (especially Python), and foundational knowledge of machine learning concepts, often supported by coursework or relevant projects. Familiarity with AI frameworks such as TensorFlow or PyTorch, experience with data analysis tools, and understanding of version control systems like Git are typically required. Curiosity, critical thinking, and effective communication help interns learn quickly, collaborate with teams, and contribute innovative ideas. These skills and qualities enable interns to contribute meaningfully to AI projects, adapt to new challenges, and build a solid foundation for a future career in AI engineering.

What are the most commonly searched types of Artificial Intelligence Engineer jobs in Indiana?

The most popular types of Artificial Intelligence Engineer jobs in Indiana are:

What cities in Indiana are hiring for Artificial Intelligence Engineer Intern jobs?

Cities in Indiana with the most Artificial Intelligence Engineer Intern job openings:

Artificial Intelligence Engineer Intern jobs near you

Artificial Intelligence Engineer (On-Site, IN)

Allied Solutions LLC

Carmel, IN • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 19 days ago

Allied Solutions rating

7.9

Company rating: 7.9 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

187th of 315 rated insurance

Job description

An Artificial Intelligence Engineer architects, fine-tunes, and deploys AI solutions to streamline operations and unearth insights, driving innovation across diverse industries. With expertise in machine learning and cognitive technologies, they build predictive models that not only solve current challenges but also anticipate future trends. Their work revolutionizes data utilization, enhancing decision-making and setting new standards for operational excellence.
*Job Duties and Responsibilities:
Hands-On Solution Implementation (50%):
  • Implementation: Configure, connect, and extend AI-enabled solutions using enterprise AI platforms, commercial products, automation tools, integrations, and lightweight supporting components.
  • AI Platform Integration: Design and build secure, reusable connections that enable enterprise AI platforms to access approved data, services, and business actions using APIs, connectors, orchestration tools, and emerging standards such as Model Context Protocol (MCP)
  • Prototyping: Build and validate proofs of concept to confirm feasibility, usability, performance, and expected value before broader implementation.
  • Production Delivery: Advance assigned solutions through testing, production readiness, launch, and operational handoff within the team's delivery priorities and practices.
  • Problem Resolution: Troubleshoot implementation issues and collaborate with platform, architecture, security, data, vendor, and business partners to resolve constraints.

Solution Discovery and Delivery Planning (15%):
  • Implementation Discovery: Work with business stakeholders and the AI Architect to understand the assigned workflow, users, pain points, desired outcomes, and measures of success.
  • Requirements and Data Readiness: Translate an approved use case into implementable requirements and acceptance criteria; identify required data, access, dependencies, and delivery constraints.
  • Workflow Validation: Validate the proposed future-state workflow through demonstrations, prototypes, and user feedback, surfacing practical implementation considerations and appropriate human decision points.
  • Delivery Planning: Provide estimates, technical findings, risks, and implementation options to support solution and delivery decisions.

Platform and Capability Solutioning (15%):
  • Capability Awareness: Maintain a strong understanding of Allied's enterprise platforms, approved technologies, data assets, integration capabilities, and reusable services.
  • Capability Fit: Evaluate candidate capabilities through hands-on research and experimentation, including generative AI, predictive machine learning, rules-based automation, analytics, and existing platforms.
  • Technical Findings: Document feasibility, performance, integration needs, implementation effort, limitations, and support considerations to inform solution decisions; work with the AI Architect and platform owners to ensure the selected approach follows enterprise patterns and guardrails.
  • Recommendations: Develop clear solution recommendations based on business fit, implementation speed, security, integration, cost, scalability, and ongoing support needs.

Responsible, Reliable, and Sustainable Implementation (10%):
  • Responsible Delivery: Implement applicable Responsible AI, privacy, security, legal, accessibility, and data-governance requirements.
  • Testing and Evaluation: Define and execute evaluations appropriate to the solution type, including business effectiveness, usability, accuracy, precision and recall where applicable, groundedness, bias, drift, failure handling, human oversight, and escalation.
  • Operational Readiness: Implement appropriate monitoring, feedback mechanisms, documentation, and support procedures.
  • .Sustainable Design: Consider maintainability, platform alignment, vendor dependencies, technical debt, and operational overhead in implementation decisions.

Adoption, Measurement, and Reuse (10%):
  • User Enablement: Partner with Enablement Lead and business teams to provide guidance, demonstrations, training, and other support needed for effective adoption.
  • Continuous Improvement: Gather usage information, user feedback, business results, and relevant platform or vendor changes to recommend improvements, simplification, scaling, replacement, or retirement.
  • Knowledge Reuse: Create and share reusable configurations, workflow patterns, implementation documentation, and lessons learned.

*Qualifications (Education, Experience, Certifications & KSA):
  • Bachelor's degree in computer science, Artificial Intelligence, Data Science, or a related field is required.
  • Master's degree preferred.
  • Relevant work experience may be considered as an equivalent for education requirements.
  • 4+ years of professional experience in AI or related fields required.
  • Experience in developing and implementing AI models and systems.
  • Experience with cloud computing services (AWS, Azure, Google Cloud) is a plus.
  • Portfolio of projects or contributions to open-source projects demonstrating expertise in AI.
  • Proficient in programming languages such as Python, R, or Java. In-depth knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NLTK).
  • Strong ability to work with large data sets and complex algorithms. Proficient in data structures, statistical modeling, and computer science fundamentals.
  • Excellent problem-solving skills and the ability to think algorithmically.
  • Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
  • Analytical and decision-making.
  • Ability to work independently and as part of a team.
  • Ability to meet deadlines and work under pressure.
  • Ability to think strategically and tactically.

#LI-ID1
The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, skills, efforts or working conditions associated with a job.
We offer our employees a robust compensation package! Our comprehensive benefits include: medical, dental and vision insurance coverage; 100% company-paid life and disability coverage, 401k options with company match, three weeks PTO by the end of the first year and much more. Allied proudly promotes from within as part of a strong commitment to providing career growth opportunities for employees of all levels. Our diverse business portfolio allows employees broad career options with the advantage of staying with the same organization.
All qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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