AI Engineer

Job Description

This role will be highly hands on and on site at Northwestern, spending time observing how teams work and learning workflows end to end. The AI Engineer will partner closely with operations, clinical, and business teams to identify where AI can add real value, then translate those real world observations into clear AI use cases and technical approaches. They will build rapid AI prototypes to test ideas, gather feedback, and refine solutions as they mature using an iterative crawl to walk to sprint approach.

Success in this role requires comfort shadowing teams, asking questions, and learning how work actually happens, along with strong communication skills to clearly articulate AI opportunities to both technical and non technical stakeholders. A proactive, curious mindset is essential for someone who is eager to learn, collaborate, and help shape how AI is adopted across the organization.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• 3–7 years of hands‑on AI/ML engineering experience, including model development, fine‑tuning, and applied use cases.
• Strong Python experience with modern ML frameworks (PyTorch, Tensor Flow, Hugging Face, scikit‑learn).
• Proven prototype/POC experience, ability to quickly build, test, and iterate on AI solutions.
• Experience fine‑tuning LLMs or domain‑specific models.
• Prior work embedding AI into business or operational workflows (healthcare, operations, enterprise environments).
• Exposure to MLOps or deployment basics (APIs, model versioning, cloud platforms).
• Familiarity with process mapping, workflow automation, or human‑in‑the‑loop systems.