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Entry Level Ai Engineer Jobs in Detroit, MI (NOW HIRING)

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*Must be okay with travel Entry Level Position: College Graduate - 2 years experience This is a ... Architect custom solutions using the appropriate technologies from control databases to AI to User ...

In this role at PwC, you will apply data, algorithms, and software engineering to build and deploy ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

Use AI-assisted development tools to accelerate development, refactoring, test creation, and ... Azure Fundamentals or entry-level Azure certification. * Familiarity with Cursor, Claude Code, etc.

Use AI-assisted development tools to accelerate development, refactoring, test creation, and ... Azure Fundamentals or entry-level Azure certification. * Familiarity with Cursor, Claude Code, etc.

As an entry-level Software Developer for this Fortune 500 automotive company, you'll be joining a ... Exposure to AI development (automation, agents, prompt engineering, etc) * Frontend development ...

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Entry Level Ai Engineer information

See Detroit, MI salary details

$27.4K

$63.5K

$108K

How much do entry level ai engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for entry level ai engineer in Detroit, MI is $63,466.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,100.00 and $71,800.00 per year, depending on experience, location, and employer.

What does an entry level AI engineer do?

An Entry Level AI Engineer assists in designing, developing, and deploying artificial intelligence models and systems under the guidance of senior engineers. Their tasks often include collecting and preprocessing data, implementing machine learning algorithms, and testing models to ensure accuracy and efficiency. They also collaborate with software developers and data scientists to integrate AI solutions into products or services, all while continuing to learn and develop their technical skills on the job.

What types of projects do entry level AI engineers typically work on, and how do they contribute to larger team goals?

Entry-level AI engineers often start by assisting with data preprocessing, building and testing machine learning models, and supporting the deployment of AI solutions. They may also help maintain existing models, write scripts to automate workflows, and contribute to documentation. These tasks are crucial for larger team projects, as they ensure data quality and provide foundational components for more advanced AI systems. Collaboration with data scientists, software engineers, and product managers is common, offering valuable exposure to cross-functional teamwork and opportunities for skill development.

What are the key skills and qualifications needed to thrive as an entry level AI engineer, and why are they important?

To thrive as an Entry Level AI Engineer, you need a solid background in computer science, mathematics (especially linear algebra and statistics), and programming languages like Python, often supported by a relevant degree. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), version control systems (like Git), and sometimes entry-level certifications in AI or data science is valuable. Strong problem-solving abilities, eagerness to learn, and teamwork skills help you stand out in this collaborative and evolving field. These skills and qualities are crucial to effectively develop AI models, adapt to new technologies, and contribute meaningfully to innovative projects.

Can you be an entry level AI engineer with no experience?

Entry level AI engineer positions often require some foundational knowledge of programming, machine learning concepts, and relevant tools like Python or TensorFlow. While prior experience is not always mandatory, demonstrating skills through projects, certifications, or coursework can improve chances of securing an entry level role.

Is AI taking entry level AI engineering jobs?

Entry level AI engineering jobs are growing as demand for AI skills increases, but competition remains high. Success in these roles typically requires knowledge of programming languages like Python, familiarity with machine learning frameworks, and a strong foundation in mathematics. Entry-level positions often focus on data preprocessing, model training, and assisting senior engineers.

Is an entry level AI engineer an entry-level job?

An entry-level AI engineer position is typically considered an entry-level job, suitable for candidates with limited professional experience in AI, machine learning, or related fields. These roles often require foundational skills in programming, data analysis, and basic understanding of AI tools and frameworks. They are designed to provide on-the-job training and skill development for newcomers to the field.

What are the most commonly searched types of Ai Engineer jobs in Detroit, MI?

The most popular types of Ai Engineer jobs in Detroit, MI are:

What cities near Detroit, MI are hiring for Entry Level Ai Engineer jobs?

Cities near Detroit, MI with the most Entry Level Ai Engineer job openings:

Infographic showing various Entry Level Ai Engineer job openings in Detroit, MI as of August 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $63,466 per year, or $30.5 per hour.

Generative AI Researcher

Tata Consultancy Services

Warren, MI • On-site

Full-time

Re-posted 16 days ago


Tata Consultancy Services rating

6.5

Company rating: 6.5 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

176th of 226 rated it services


Job description

Job Summary:

We are seeking a highly skilled and creative Entry Level - Generative AI Engineer to apply state-of-the-art generative models to solve complex challenges in automotive engineering. This role focuses on creating intelligent agents that leverage generative capabilities for reasoning, planning, and executing complex tasks autonomously. The ideal candidate will bridge the gap between generative AI's creative potential and agentic AI's autonomous action, developing systems that can understand, reason, and act in dynamic environments.

Key Responsibilities

Integrated AI System Development:

Design and build AI agents that utilize large language models for reasoning and decision-making

Develop systems where generative AI components enable sophisticated planning and problem-solving

Create autonomous agents capable of using tools, APIs, and external systems through generative interfaces

Implement multi-agent systems where generative AI facilitates communication and collaboration

Generative AI Capabilities:

Fine-tune and optimize large language models for specific agentic tasks

Develop prompt engineering strategies for complex reasoning and chain-of-thought processes

Implement RAG (Retrieval-Augmented Generation) systems to enhance agent knowledge and context

Create generative models for code generation, content creation, and strategic planning within agent frameworks

Agent Architecture & Autonomy:

Build reflective agents that can critique and improve their own reasoning processes

Design goal-oriented systems that use generative AI for planning and adaptation

Implement memory architectures that allow agents to learn from experience and maintain context

Develop safety mechanisms and oversight for autonomous generative agents

Multi-Modal Agent Systems:

Integrate vision, language, and action capabilities within agent frameworks

Develop agents that can process and generate across multiple modalities (text, image, audio)

Create embodied agents that interact with digital and physical environments

Research & Innovation: Stay current with the latest academic research and open-source advancements in generative AI. Prototype new ideas and conduct experiments to validate their feasibility and impact.

Education: Ph.D in Computer Science, Electrical Engineering, Mechanical Engineering or related streams.

Technical Proficiency:

Experience with generative AI (LLMs, diffusion models, generative architectures)

Experience with agentic AI systems, reinforcement learning, or autonomous systems

Strong programming skills in Python and experience with AI/ML frameworks (PyTorch, TensorFlow)

Experience with LangChain, AutoGPT, Microsoft Autogen, or similar agent frameworks

Proficiency with transformer architectures and fine-tuning techniques

Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities

Experience with RAG systems, vector databases, and knowledge retrieval

Knowledge of reinforcement learning, planning algorithms, and decision-making systems

Familiarity with multi-agent systems and emergent behavior

Ph.D


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