AI & Automation Intern (Summer 2027)

Sargent & Lundy•Chicago, IL

1d•$18 - $30•Hybrid

About The Position

At Sargent & Lundy, AI is integral to transforming engineering expertise into smarter, faster solutions for the power and energy sector. This internship focuses on applying AI and automation to real projects, addressing tangible problems. Interns will collaborate with experienced engineers and data professionals on the Enterprise Data & Analytics team. The role involves understanding business problems, evaluating AI/automation solutions, and building/testing working prototypes. Emphasis is placed on critical thinking, learning quickly, and utilizing modern AI advancements like agents, retrieval-augmented workflows, and AI-assisted development. This position offers a hybrid schedule, requiring 3 days per week in the downtown Chicago, IL office and 2 days remote from home.

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).
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

Exposure to frameworks like TensorFlow, PyTorch, or LangChain.
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

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.
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.
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 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.
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.