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Stellantis
Principal AI Engineer
Career Insights for Natural Language Processing Engineer
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What they do
A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
$121,879 / year median in Michigan
Job Description
We are seeking a Principal AI Engineer with deep, handson experience in Large Language Models (LLMs) to lead the design, development, and deployment of enterprisegrade AIpowered automation systems across the organization.
This role goes beyond experimentation. You will own and deliver productionscale AI solutions, analyze complex internal workflows, identify highvalue 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, highimpact AI systems used at scale.
Work closely with crossfunctional teams to define problem statements, data requirements, system boundaries, and solution approaches.
Architect and implement endtoend LLM systems, including:
Prompt pipelinesAgentbased architecturesRetrievalAugmented Generation (RAG) systemsInternal AI services and APIsIntegrate commercial and opensource LLMs (e.g., OpenAI, Anthropic, Databricks, opensource models) into enterprise systems and products.
Drive model evaluation, prompt optimization, and system reliability improvements based on realworld usage.
Establish and maintain monitoring, logging, and evaluation frameworks for LLMdriven applications.
Partner with product, operations, security, and engineering teams to map workflows and identify highROI 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.
Proven, production experience with Large Language Models, including:
Prompt engineering and prompt optimizationModel integration and orchestrationEvaluation and reliability tuningStrong 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 nontechnical stakeholders.
Strong written and spoken English.
Experience building meaningful visualizations and explaining model behavior and results.
Background in data mining, analytics, or decisionsupport systems.
Experience with regression, supervised and unsupervised learning, and applied ML in production contexts.
Prior experience with automotive, IoT, or largescale industrial data.
Contributions to opensource projects or published technical work.
Experience operating AI systems under enterprise governance, security, and compliance constraints.
We are seeking a Principal AI Engineer with deep, handson experience in Large Language Models (LLMs) to lead the design, development, and deployment of enterprisegrade AIpowered automation systems across the organization.
This role goes beyond experimentation. You will own and deliver productionscale AI solutions, analyze complex internal workflows, identify highvalue 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, highimpact AI systems used at scale.
Work closely with crossfunctional teams to define problem statements, data requirements, system boundaries, and solution approaches.
Architect and implement endtoend LLM systems, including:
Prompt pipelinesAgentbased architecturesRetrievalAugmented Generation (RAG) systemsInternal AI services and APIsIntegrate commercial and opensource LLMs (e.g., OpenAI, Anthropic, Databricks, opensource models) into enterprise systems and products.
Drive model evaluation, prompt optimization, and system reliability improvements based on realworld usage.
Establish and maintain monitoring, logging, and evaluation frameworks for LLMdriven applications.
Partner with product, operations, security, and engineering teams to map workflows and identify highROI 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. At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard... For full info follow application link. Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled.
This role goes beyond experimentation. You will own and deliver productionscale AI solutions, analyze complex internal workflows, identify highvalue 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, highimpact AI systems used at scale.
KEY RESPONSIBILITIES
Lead the design, development, and deployment of LLMbased automation solutions across multiple business functions.Work closely with crossfunctional teams to define problem statements, data requirements, system boundaries, and solution approaches.
Architect and implement endtoend LLM systems, including:
Prompt pipelinesAgentbased architecturesRetrievalAugmented Generation (RAG) systemsInternal AI services and APIsIntegrate commercial and opensource LLMs (e.g., OpenAI, Anthropic, Databricks, opensource models) into enterprise systems and products.
Drive model evaluation, prompt optimization, and system reliability improvements based on realworld usage.
Establish and maintain monitoring, logging, and evaluation frameworks for LLMdriven applications.
Partner with product, operations, security, and engineering teams to map workflows and identify highROI 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.
Basic Qualifications:
Bachelor's degree in AI, Machine Learning, Computer Science, Statistics, or a related fieldA minimum of 8 years of professional experience in software engineering, AI, or machine learning, including a minimum of 3 years of significant handson work on LLMbased systems.Proven, production experience with Large Language Models, including:
Prompt engineering and prompt optimizationModel integration and orchestrationEvaluation and reliability tuningStrong 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 nontechnical stakeholders.
Strong written and spoken English.
Preferred Qualifications:
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 decisionsupport systems.
Experience with regression, supervised and unsupervised learning, and applied ML in production contexts.
Prior experience with automotive, IoT, or largescale industrial data.
Contributions to opensource projects or published technical work.
Experience operating AI systems under enterprise governance, security, and compliance constraints.
We are seeking a Principal AI Engineer with deep, handson experience in Large Language Models (LLMs) to lead the design, development, and deployment of enterprisegrade AIpowered automation systems across the organization.
This role goes beyond experimentation. You will own and deliver productionscale AI solutions, analyze complex internal workflows, identify highvalue 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, highimpact AI systems used at scale.
KEY RESPONSIBILITIES
Lead the design, development, and deployment of LLMbased automation solutions across multiple business functions.Work closely with crossfunctional teams to define problem statements, data requirements, system boundaries, and solution approaches.
Architect and implement endtoend LLM systems, including:
Prompt pipelinesAgentbased architecturesRetrievalAugmented Generation (RAG) systemsInternal AI services and APIsIntegrate commercial and opensource LLMs (e.g., OpenAI, Anthropic, Databricks, opensource models) into enterprise systems and products.
Drive model evaluation, prompt optimization, and system reliability improvements based on realworld usage.
Establish and maintain monitoring, logging, and evaluation frameworks for LLMdriven applications.
Partner with product, operations, security, and engineering teams to map workflows and identify highROI 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. At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard... For full info follow application link. Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled.