Principal AI Engineer

Stellantis•Auburn Hills, MI

About The Position

We are seeking a Principal AI Engineer with deep, hands‑on experience in Large Language Models (LLMs) to lead the design, development, and deployment of enterprise‑grade AI‑powered automation systems across the organization. This role goes beyond experimentation. You will own and deliver production‑scale AI solutions, analyze complex internal workflows, identify high‑value automation opportunities, and architect intelligent systems that materially improve efficiency, accuracy, and scalability. This position is ideal for a seasoned engineer (10+ years) who combines strong technical depth, architectural judgment, and a product mindset, and who enjoys building practical, high‑impact AI systems used at scale.

Requirements

Bachelor’s degree in AI, Machine Learning, Computer Science, Statistics, or a related field
A minimum of 8 years of professional experience in software engineering, AI, or machine learning, including a minimum of 3 years of significant hands‑on work on LLM‑based systems.
Proven, production experience with Large Language Models, including: Prompt engineering and prompt optimization, Model integration and orchestration, Evaluation and reliability tuning.
Strong proficiency in Python and modern AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, LangChain, similar ecosystems).
Solid background in deep learning and applied machine learning.
Strong analytical and mathematical foundation relevant to ML systems.
Experience designing systems that balance performance, scalability, cost, and accuracy.
Ability to communicate complex technical concepts clearly to technical and non‑technical stakeholders.
Strong written and spoken English.

Nice To Haves

Master’s degree in AI, Machine Learning, Computer Science, Statistics, or a related field (or equivalent professional experience).
PhD or additional advanced degree in AI, Machine Learning, Computer Science, Statistics, or related fields.
Experience building meaningful visualizations and explaining model behavior and results.
Background in data mining, analytics, or decision‑support systems.
Experience with regression, supervised and unsupervised learning, and applied ML in production contexts.
Prior experience with automotive, IoT, or large‑scale industrial data.
Contributions to open‑source projects or published technical work.
Experience operating AI systems under enterprise governance, security, and compliance constraints.

Responsibilities

Lead the design, development, and deployment of LLM‑based automation solutions across multiple business functions.
Work closely with cross‑functional teams to define problem statements, data requirements, system boundaries, and solution approaches.
Architect and implement end‑to‑end LLM systems, including: Prompt pipelines, Agent‑based architectures, Retrieval‑Augmented Generation (RAG) systems, Internal AI services and APIs.
Integrate commercial and open‑source LLMs (e.g., OpenAI, Anthropic, Databricks, open‑source models) into enterprise systems and products.
Drive model evaluation, prompt optimization, and system reliability improvements based on real‑world usage.
Establish and maintain monitoring, logging, and evaluation frameworks for LLM‑driven applications.
Partner with product, operations, security, and engineering teams to map workflows and identify high‑ROI automation opportunities.
Ensure all AI solutions meet enterprise standards for data privacy, security, compliance, and governance.
Act as a technical mentor and thought leader, setting best practices for LLM engineering and applied AI.
Stay current with advances in LLMs, agent frameworks, AI infrastructure, and applied research—and translate them into pragmatic solutions.