AI Engineering Intern; LLM

Position: AI Engineering Intern (LLM)
Company Description

Veolia in North America is the top-ranked environmental company in the United States for three consecutive years, and the country's largest private water operator and technology provider as well as hazardous waste and pollution treatment leader. It offers a full spectrum of water, waste, and energy management services, including water and wastewater treatment, commercial and hazardous waste collection and disposal, energy consulting and resource recovery.

Veolia helps commercial, industrial, healthcare, higher education and municipality customers throughout North America. Headquartered in Boston, Veolia has more than 10,000 employees working at more than 350 locations across North America.

Job Description

Student Exploration and Experience Development (SEED) is a 12-week internship opportunity at Veolia for students to gain hands-on experience in sustainability and ecological transformation. They will work on real-world projects, receive mentorship from industry professionals, and participate in workshops and networking events. The program aims to nurture talent, promote innovation, and foster meaningful connections between students and industry professionals. Overall, the SEED program provides students with the skills, knowledge, and connections needed to make a positive impact in the industry.

Program Dates:
June 1, 2026 to August 21, 2026.

Position

Purpose:

We are seeking a motivated AI Engineering intern to support the development and implementation of an AI-powered deep research agent for

This role offers hands-on experience with cutting-edge large language models, cloud infrastructure, and enterprise software development.

Primary Duties/Responsibilities:

* Large Language Models (LLMs):

* Understanding and working with commercial/proprietary LLMs such as Gemini( Google), GPT(OpenAI) and Claude Sonnet (Anthropic) for high performance, large context, and multimodal tasks.

* Familiarity with open-source/self-hosted LLMs like Llama from Meta and Mixtral from (Mistral AI).

* Design & Planning Phase:

* Requirements Gathering:
Using Confluence for documentation and collaboration.

* Architecture Design:
Creating system diagrams and workflows with Lucidchart.

* Prototyping:
Designing UI/UX prototypes in Figma.

* Project Management:
Tracking tasks and progress in Jira.

* Data Preparation & Management:
Cleaning, transforming, and organizing data for use in AI/ML workflows.

* Development Framework & Tools:

* Core LLM Frameworks:
Using Lang Chain or Llama Index for orchestrating LLM applications.

* Agent Frameworks:
Building multi-agent systems with Semantic Kernel, CrewAI, and Lang Graph.

* Prompt Management:
Managing and optimizing prompts with Lang Smith.

* Vector Databases & Search:

* Implementing semantic search and retrieval using Vertex AI Vector DBs

* Backend Development:

* API Framework:
Developing RESTful APIs with FastAPI (Python).

* Message Queue:
Integrating asynchronous communication with Apache Kafka and Redis Streams.

* Frontend Development:

* Web Framework:
Building user interfaces with React or Angular.

* UI Components:
Utilizing Material-UI for consistent, modern UI elements.

* IDE:
Using Google AI Studio for AI application development.

* Development Tools:

* IDE:
Writing and debugging code in VS Code.

* AI Assistants:
Leveraging Git Hub Copilot and Cursor for code suggestions and productivity.

* Version Control Managing code with Git Hub, or Git Lab.

* Code Quality:
Ensuring code quality and standards with Sonar Qube, ESLint, and Pylint.

* Model Fine-tuning & Customization:

* Fine-tuning Platforms:
Using Vertex AI Tuning for model customization.

* Training Frameworks:
Training and experimenting with models in PyTorch, Tensor Flow, or JAX.

* Efficient Training:
Applying parameter-efficient fine-tuning (PEFT) methods like LoRA and QLoRA.

* Synthetic Data:
Generating synthetic data.

* Evaluation:
Assessing models with HELM, lm-evaluation-harness, and custom benchmarks.

* Testing Stack:

* LLM-Specific Testing:
Using RAGAS, and Deep Eval for LLM evaluation;
Lang Smith Evaluators for prompt testing; hallucination detection.

* Deployment &

Infrastructure:

* Containerization:
Packaging applications with…