AI Engineer Intern

Location: Bengaluru, India / Naperville, IL USA

Duration: 3–6 months (with potential for full-time conversion)

Company Description

Cloudbyz is a next-generation eClinical platform, built natively on Salesforce, designed to modernize and streamline clinical trial operations for life sciences organizations. The platform eliminates silos by integrating critical functions like CTMS, EDC, eTMF, RTSM, ePRO & eCOA, and more into a single, comprehensive solution.

Trusted by pharmaceutical companies, biotechs, medical device innovators, and CROs, Cloudbyz ensures real-time oversight, operational efficiency, and regulatory compliance throughout the trial lifecycle. Known for its secure, scalable, and easily configurable platform, Cloudbyz helps organizations achieve faster study start-up, centralized data management, and improved cross-functional collaboration.

The company is based in Warrenville, IL, and is committed to advancing smarter trials worldwide.

Role Summary

The AI Engineer Intern will work closely with senior AI engineers, product managers, and domain experts to help design, build, and test AI-powered features and AI Agents for enterprise SaaS platforms. You will gain hands-on experience applying machine learning, generative AI, and automation to real-world problems in clinical research, pharmacovigilance, and regulated environments.

This role is ideal for someone who is technically curious, hands-on, and eager to learn how AI is built and deployed in production systems.

Key Responsibilities

AI & Machine Learning Development

Assist in building and testing ML models, LLM-based workflows, and AI Agents
Work on data preparation, labeling, and feature engineering
Support experimentation with prompt engineering, embeddings, and retrieval techniques
Evaluate model performance and document results

Generative AI & Automation

Help develop GenAI use cases such as document classification, summarization, extraction, and chat interfaces
Assist in integrating LLMs via APIs into backend services
Support Human-in-the-Loop workflows for AI validation and review
Contribute to AI safety, accuracy, and explainability efforts

Engineering & Integration

Work with engineers to integrate AI components into web applications and APIs
Programming and Development on Google Vertex AI and AWS Bedrock using Python and other programming languages
Engineering and deployment using Docker and other CI/CD tools
Assist with building REST APIs, background jobs, and AI pipelines
Participate in code reviews, testing, and documentation
Learn how AI systems are deployed, monitored, and scaled

Research & Innovation

Research new AI techniques, tools, and frameworks
Prototype ideas quickly and validate feasibility
Stay current with developments in AI, ML, and LLM ecosystems

Required Qualifications

Education

Currently pursuing or recently completed a degree in Computer Science, AI/ML, Data Science, Engineering, or related field

Technical Skills (Beginner–Intermediate)

Programming experience in Python (preferred)
Basic understanding of machine learning concepts
Familiarity with libraries such as NumPy, Pandas, Scikit-learn, PyTorch, or TensorFlow
Basic knowledge of APIs and data structures
Exposure to Git and version control

Soft Skills & Attributes

Strong curiosity and willingness to learn
Ability to work independently and in a team
Good problem-solving and analytical thinking
Clear communication and documentation skills
Interest in real-world, production AI (not just notebooks)

Nice to Have

Exposure to LLMs, LangChain, vector databases, or RAG
Experience with cloud platforms (AWS, GCP, Azure)
Familiarity with SQL and databases
Interest in healthcare, life sciences, or regulated software
Prior internships, hackathons, or personal AI projects

What You Will Learn

How AI products are built end-to-end in an enterprise SaaS environment
Applying GenAI and ML to documents, workflows, and automation
Working with real customer problems and datasets
Best practices for scalable, reliable, and responsible AI
Collaboration across Product, Engineering, and Domain teams