Entry Level Artificial Intelligence Engineer

Job Description:

My client, a leading AI Consulting firm, is seeking early career professionals for a Artificial Intelligence Engineer. This is a client facing role where you will be building pipelines, writing code, supporting analysis, documenting systems, and contributing to client delivery with tasks that matter. You will be given increasing responsibility as you demonstrate that you can be trusted with it. The right person for this role is someone who is technically sharp, genuinely curious, takes their work seriously, and understands that the foundation of a great consulting career is built on reliability, communication, and a commitment to quality - not just technical skill alone.

Project-Specific Expectations
Delivery Contribution
* Contribute to assigned tasks within client engagements - writing code, building pipelines, running analysis, or supporting configuration - under the direction of senior consultants and architects.
* Take ownership of the tasks you are given: completing them to the standard described, checking your own work before calling it done, and raising questions early rather than submitting work you are unsure about.
* Meet your commitments consistently - if you say something will be done by a certain time, it is done by that time, or you have communicated well in advance that you need more time or have run into a blocker.
* Learn the client's environment quickly: their tools, their data, their processes, and the business context that makes some things matter more than others.
* Support documentation tasks - data dictionaries, pipeline runbooks, meeting notes, test logs - with the care and accuracy that makes them genuinely useful rather than boxes checked.
* Technical Learning & Application
* Apply your technical foundations in SQL, Python, and cloud tooling to real problems, learning to adapt what you know to the constraints and conventions of each client's environment.
* Ask for help when you are stuck, but come to the conversation having already attempted to solve the problem - specific questions get better answers than general ones, and engineers who show their work earn more respect than those who delegate confusion upward.
* Actively learn the tools, platforms, and patterns in use on your engagement - dbt, Airflow, Snowflake, cloud services, AI frameworks - treating each project as an opportunity to extend your technical depth.
* Read and understand existing code and systems before modifying them - because the most common source of new problems in delivery is changes made without understanding what was already there.
* Begin developing the habit of writing code and documentation that your teammates can read and use without asking you to explain it.

Professional Conduct & Client Presence
* Show up prepared: read the agenda before meetings, know the status of your work before standups, and have your questions ready before asking for someone's time.
* Communicate proactively: let people know your status before they ask, surface blockers early enough for them to be resolved without disrupting the team, and be honest about what you do and do not know.
* Behave professionally in all client interactions - written and spoken - recognizing that you are representing us in every message, meeting, and deliverable you produce.
* Receive feedback well: listen to it, act on it, and treat it as the most direct path to becoming someone whose work doesn't need to be reviewed twice.
* Be someone your team can count on - not the most technically advanced person in the room, but someone whose word means something and whose work holds up.

Growth & Initiative
* Take initiative on your own development: identify the gaps in your knowledge that are limiting your contribution and actively close them, rather than waiting for someone to schedule training.
* Observe how senior consultants operate - how they communicate, how they plan their work, how they handle uncertainty - and develop your own practice from those observations.
* Volunteer for tasks that stretch your current capabilities, with the understanding that this is how entry-level practitioners accelerate past their peers.
* Contribute to team quality in small ways: catching a typo in a document before it goes to the client, suggesting a cleaner approach to a piece of code, flagging something that doesn't look right in a dataset - because attention to detail at this level is a signal that scales.
* Identify process and technical improvements within your engagement and raise them clearly - with a proposed solution, not just an identified problem.
* Contribute to internal knowledge base: documenting patterns, lessons learned, and reusable accelerators that make the next engagement better.

REQUIREMENTS
* A bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or a related technical field - or equivalent demonstrated technical competence through project work, bootcamp, or professional experience.
* Working knowledge of SQL: able to write, read, and debug queries against real datasets without assistance on straightforward tasks.
* Foundational Python skills: able to write scripts, work with data using pandas or similar libraries, and read and modify existing code.
* Some exposure to cloud platforms, data tools, or software development workflows - whether through coursework, personal projects, or prior employment.
* Strong written communication skills: able to write clear, professional emails, status updates, and documentation without requiring significant editing.
* A genuine interest in how technology is applied to solve real business problems - not just in building technical things for their own sake.
* The professional reliability to show up prepared, meet deadlines, communicate proactively, and take feedback seriously.

Preferred
* Internship, co-op, or project experience in a professional technical environment - any exposure to the difference between classroom work and production work is valuable context.
* Familiarity with modern data tools such as dbt, Airflow, Snowflake, or BigQuery - even at a conceptual or self-study level.
* Exposure to AI and ML concepts: what models are, how they are trained and evaluated, and where they succeed and fail in practice.
* Experience with version control (Git) and basic software development workflows.
* A portfolio of technical projects - academic or personal - that demonstrates you build things with real data and care about how they work.

This role is not able to provide sponsorship now or in the future.
This role is onsite M-TH in Addison