AI ML Cloud Engineering Intern

Summer Internship Opportunity - AI / ML Cloud Engineering Intern – AI Compass Program 

Location: Remote 

Duration: Summer Internship (Full-time or Part-time, 10–12 weeks) 

Potential Outcome: Opportunity for future part-time engagement or full-time employment based on performance 

About eSystems 

eSystems is a technology consulting and systems integration firm that designs and implements digital modernization solutions for U.S. State and Local Government agencies. The company specializes in large-scale Health and Human Services (HHS) and Workforce systems that support programs such as Medicaid, SNAP, TANF, and Unemployment Insurance (UI). 

As part of its AI Compass Program, eSystems is developing next-generation AI-enabled platforms that improve citizen experience and access to government services. These solutions combine cloud computing, conversational AI, machine learning, and modern web platforms to build intelligent digital service systems. One of the key initiatives is a cloud-based Omni-Channel Platform designed to support multiple communication channels such as chat, SMS, web portals, and voice systems while leveraging AI to assist both citizens and government workers. 

This internship provides students with an opportunity to work on real-world AI-powered applications used in large-scale public service systems, while gaining experience in modern cloud architecture and machine learning practices. 

Internship Role Overview 

The AI/ML Cloud Engineering Intern will support development activities within the AI Compass Program. Interns will work with senior engineers and technology architects to build and test components of AI-enabled cloud applications, including conversational systems, data pipelines, and intelligent automation services. 

The role is designed for senior undergraduate or graduate students interested in gaining hands-on experience with modern AI development tools, cloud infrastructure, and real-world software engineering practices. 

Interns will contribute to exploratory development, prototyping, testing, and enhancement of AI-driven capabilities that support citizen-facing digital services. 

Key Responsibilities 

AI and Machine Learning Development 

Interns will assist in building and evaluating AI-driven capabilities used in conversational and knowledge-based systems. Activities may include: 

Developing and testing prompt-based interactions with large language models (LLMs) 

Assisting with the design of retrieval-augmented generation (RAG) pipelines that combine search, vector embeddings, and language models 

Creating scripts and utilities to evaluate model outputs for accuracy, bias, and reliability 

Supporting development of knowledge bases and semantic search systems using vector embeddings and document indexing techniques 

Experimenting with natural language processing techniques for question answering, summarization, and information retrieval 

Students may work with tools such as AWS Bedrock, OpenAI APIs, LangChain-style frameworks, or similar AI development platforms. 

Cloud and Application Engineering 

Interns will contribute to the development of cloud-native services that support AI-enabled applications. Responsibilities may include: 

Developing backend services and APIs using Python, Java, or other modern programming languages 

Implementing serverless or microservice-based components using cloud services such as AWS Lambda, API Gateway, and managed databases 

Assisting with conversational workflow development using tools such as Amazon Lex or similar conversational platforms 

Supporting integration between AI services, application logic, and user-facing web interfaces 

Writing and testing reusable code components that support system integration and automation 

Interns will also gain exposure to common DevOps practices, including source control, automated testing, and continuous integration pipelines. 

Data and Platform Development 

Interns may also support platform development activities such as: 

Processing and organizing structured and unstructured datasets used by AI models 

Building data pipelines and scripts that prepare documents for search, retrieval, or machine learning workflows 

Integrating application services with relational or NoSQL databases 

Supporting front-end or middleware components used in web-based platforms 

Ideal Candidate Profile 

Faculty and Career Center staff are encouraged to recommend students who demonstrate a strong foundation in computer science, data science, or artificial intelligence and who are interested in applying these skills to real-world systems. 

Academic Background 

Senior undergraduate or graduate student pursuing a degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, or related technical discipline 

Technical Skills 

Students should have experience gained through coursework, research, or personal projects in areas such as: 

Programming in Python, Java, C#, or similar languages 

Building software projects, APIs, or web applications 

Working with databases such as SQL or NoSQL systems 

Basic familiarity with cloud computing platforms (AWS, Azure, or Google Cloud) 

Experience using tools such as Git or GitHub for source control 

AI/Data Science Exposure 

Ideal candidates will have completed coursework or projects involving one or more of the following: 

Machine learning or statistical modeling 

Natural language processing or text analysis 

Generative AI or large language models 

Data analysis, visualization, or feature engineering 

Prior experience with AI frameworks or libraries such as PyTorch, TensorFlow, Scikit-learn, HuggingFace, or LangChain is beneficial but not required. 

Professional Skills 

Strong analytical and problem-solving capabilities 

Ability to learn new technologies quickly 

Interest in building practical, real-world AI applications 

Strong written and verbal communication skills 

Ability to work collaboratively in an Agile or iterative development environment 

What Students Gain 

Students participating in the internship will gain: 

Hands-on experience building AI-enabled cloud applications and intelligent digital platforms 

Exposure to modern AI tools including LLMs, conversational AI systems, and knowledge retrieval pipelines 

Experience with cloud-native architectures, microservices, and serverless development 

Mentorship from experienced engineers, architects, and AI practitioners 

Understanding of how advanced technologies are applied to improve large-scale public service systems 

High-performing interns may be considered for extended internships, part-time engagements during the academic year, or full-time employment after graduation. 

Application Information 

Interested students should submit: 

Resume highlighting relevant coursework and technical projects 

Optional GitHub or portfolio links demonstrating programming or AI-related work 

A brief note describing interest in AI development, cloud technologies, or public-sector technology systems