AI & Automation Intern (Summer 2026)

Sargent & Lundy•Chicago, IL

20h•$18 - $27•Hybrid

About The Position

At Sargent & Lundy, AI isn’t a side experiment — it’s how we’re transforming 130+ years of engineering expertise into smarter, faster solutions for the power and energy sector. We’re looking for an AI & Automation Intern who wants to be part of that — someone who sees a broken process or an unanswered question and immediately starts thinking about how to fix it. You’ll join experienced engineers and data professionals on our Enterprise Data & Analytics team, contributing to real projects where AI and automation solve tangible problems in the power and energy sector. This isn’t about building models in a vacuum. You’ll help us understand messy business problems, evaluate whether AI, automation, or a combination of both is the right fit, and then help build and test working solutions. You’ll be expected to ask good questions, think critically about the tools and approaches you choose, and learn quickly in a fast-moving space. The AI landscape has changed — agents, retrieval-augmented workflows, and AI-assisted development are no longer experimental. We want someone who’s already leaning into these shifts and eager to apply them to real-world challenges.

Requirements

Currently pursuing a bachelor’s degree in Computer Science, Computer Engineering, Data Science, or a related field.
Problem-solving mindset — you can take an ambiguous question, break it into pieces, and make progress even when there’s no clear playbook. You’re comfortable with “I’ll figure it out.”
Solid academic foundation in core technical areas (e.g., data structures & algorithms, linear algebra, probability/statistics).
Proficiency in Python for scripting, data analysis, or application development.
Familiarity with AI/ML concepts (supervised/unsupervised learning, regression, classification, clustering) and a basic sense of when each is useful.
Experience with common Python libraries (e.g., NumPy, pandas, scikit-learn). Exposure to frameworks like TensorFlow, PyTorch, or LangChain is a plus.
Comfortable using AI-assisted development tools (GitHub Copilot, Cursor, or similar) as a regular part of how you write and iterate on code.
Familiarity with Git/GitHub and modern development workflows.
Curious and coachable — you actively seek feedback, learn from mistakes, and adapt quickly.
Strong written and verbal communication skills — you can explain your thinking clearly, not just your code.

Nice To Haves

Hands-on experience with agentic AI frameworks, RAG pipelines, or prompt engineering.
Experience with data visualization (Matplotlib, Seaborn, Plotly) or interactive notebooks (Jupyter).
Exposure to cloud platforms (Azure, AWS, or GCP) or development practices like CI/CD and containerization.
Experience with automation or workflow tools (Power Automate, RPA, or scripting-based process automation).
Participation in hackathons, side projects, research, or open-source work — especially examples where you identified a problem and built something to address it.
A portfolio, GitHub profile, or project write-up that shows how you think through problems, not just what you shipped.
Demonstrated initiative or ability to juggle academics, projects, and extracurriculars effectively.

Responsibilities

Understand the Problem First
Collaborate with team members and business stakeholders to understand challenges before jumping to solutions — learning how to translate a business need into a well-scoped technical approach.
Ask clarifying questions, gather context, and help define what “success” looks like for a given project.
Build & Iterate on Solutions
Contribute to the development of AI-driven solutions and automations using Python and modern frameworks, with guidance from senior team members.
Help evaluate different approaches (AI, automation, hybrid) based on the problem at hand — not just default to the most complex option.
Assist in building and testing notebooks, scripts, lightweight APIs, or workflow automations that deliver practical value.
Work with Data Thoughtfully
Support data collection, cleaning, transformation, and feature engineering — learning to treat data prep as a critical problem-solving step.
Help identify data quality issues, gaps, or biases and flag them with recommended next steps.
Use Modern AI Tools Effectively
Use AI-assisted development tools (GitHub Copilot, Cursor, code generation models) as part of your daily workflow — while thinking critically about what they produce.
Explore and contribute to agentic AI workflows, retrieval-augmented generation (RAG) patterns, and prompt engineering as part of solution development.
Test, Learn & Communicate
Help with solution testing, evaluation, and performance analysis — focusing on understanding results, not just reporting numbers.
Participate in team discussions, bringing ideas, questions, and a willingness to challenge assumptions constructively.
Contribute to documentation, reports, and presentations that explain what was tried, what worked, what didn’t, and what you’d do differently.