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Apprentice Generative Ai Engineer Jobs (NOW HIRING)

Prosum Inc.

Generative AI Engineer

Fort Worth, TX · On-site

$120K - $170K/yr

Generative AI Engineer Location: Remote (U.S.) Salary Range: $120k to $170k About the Role We are seeking a highly skilled Generative AI Engineer to lead the end-to-end delivery of production-grade ...

Carlin Shayn Inc

Generative AI Engineer

Phoenix, AZ

Generative AI Engineer (Agentic AI / LLM Solutions) Location: Phoenix, AZ / New York City, NY (Hybrid/Onsite) Duration: 12+ Months Contract Interview Mode: Video Interview Position Overview We are ...

Prophecy Technologies

Generative AI Engineer

Tampa, FL · On-site

The role is for an AI Engineer focused on designing, developing, and implementing machine learning and AI models, particularly Generative AI applications and Agentic AI solutions. The engineer will ...

OpenDataJobs

Senior Generative AI Engineer

Potomac, MD · On-site +1

$57.25 - $73.75/hr

Our client is seeking a Senior Generative AI Engineer to drive internal initiatives and provide AI engineering support for a major federal agency. The role centers on developing cloud-native ...

OpenDataJobs

Senior Generative AI Engineer

Washington, DC · On-site +1

$62.50 - $80.75/hr

Our client is seeking a Senior Generative AI Engineer to drive internal initiatives and provide AI engineering support for a major federal agency. The role centers on developing cloud-native ...

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How much do apprentice generative ai engineer jobs pay per hour?

As of Jun 11, 2026, the average hourly pay for apprentice generative ai engineer in the United States is $18.39, according to ZipRecruiter salary data. Most workers in this role earn between $14.66 and $21.88 per hour, depending on experience, location, and employer.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior AI engineers, research directors, or executive positions in tech companies. These roles often require advanced skills in machine learning, deep learning, and experience with large-scale AI projects, along with leadership responsibilities and specialized certifications.

What engineers make $500,000?

Senior engineers in specialized fields such as software, data science, or AI engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries. Roles like senior AI or machine learning engineers often reach this level through bonuses, stock options, and other compensation components.

How can I become an AI engineer with no experience?

To become an AI engineer with no experience, start by learning programming languages like Python and studying machine learning fundamentals through online courses or tutorials. Gaining hands-on experience with tools such as TensorFlow or PyTorch and building small projects can help develop practical skills; internships or entry-level roles can also provide valuable industry exposure.

What is the difference between Apprentice Generative Ai Engineer vs Junior Data Scientist?

AspectApprentice Generative Ai EngineerJunior Data Scientist
Required CredentialsBasic understanding of AI, programming, and machine learning fundamentalsBachelor's degree in Data Science, Computer Science, or related field
Work EnvironmentHands-on training in AI development teams, often in tech companies or startupsData analysis, modeling, and reporting in various industries
Employer & Industry UsageTech companies focusing on AI products, research labs, startupsFinance, healthcare, marketing, and other data-driven sectors

The Apprentice Generative Ai Engineer role is focused on gaining practical experience in AI development, often with mentorship, while a Junior Data Scientist typically handles data analysis and modeling tasks. Both roles require foundational knowledge, but the Apprentice role emphasizes learning and skill development in generative AI technologies.

Which 3 jobs will survive AI?

For an Apprentice Generative AI Engineer, roles that require complex problem-solving, creativity, and emotional intelligence, such as AI ethics specialists, AI trainers, and human-centered design experts, are likely to persist. These jobs involve tasks that are difficult for AI to fully replicate and often require interdisciplinary skills and human judgment. Continuous learning and developing expertise in AI tools and ethical considerations will enhance job security in this field.
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Apprentice Generative Ai Engineer jobs near you
Infographic showing various Apprentice Generative Ai Engineer job openings in the United States as of June 2026, with employment types broken down into 89% Full Time, 9% Part Time, and 2% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $38,247 per year, or $18.4 per hour.
Generative AI Engineer

Generative AI Engineer

Prosum Inc.

Fort Worth, TX • On-site

$120K - $170K/yr

Full-time

Posted 15 days ago

Job description

Job Description
Generative AI Engineer
Location: Remote (U.S.)
Salary Range: $120k to $170k
About the Role
We are seeking a highly skilled Generative AI Engineer to lead the end-to-end delivery of production-grade AI systems. This role is responsible for designing, building, deploying, and continuously optimizing scalable generative AI solutions that integrate seamlessly with enterprise systems. You will act as a technical authority, shaping best practices and driving innovation across AI initiatives.
What You'll Do
  • Own the full lifecycle of generative AI systems, from architecture and development to deployment, monitoring, and optimization
  • Design and build LLM-powered applications, including agent-based workflows, multi-step RAG pipelines, and enterprise AI solutions
  • Establish and enforce engineering standards across prompt design, orchestration, structured outputs, and workflow lifecycle management
  • Serve as a technical leader for GenAI, guiding architecture decisions and best practices
  • Integrate AI systems with enterprise data, internal APIs, and cloud-native services
  • Evaluate and select models, implement routing strategies, and optimize for latency, cost, and performance
  • Continuously assess emerging AI tools and improve existing systems
  • Own system performance across reliability, scalability, throughput, and cost efficiency
  • Build and maintain observability frameworks (monitoring, tracing, logging, alerting)
  • Design and manage CI/CD pipelines, including versioning and release processes
  • Lead incident response and root cause analysis, implementing long-term fixes
  • Develop evaluation pipelines for LLM outputs, including regression testing and failure analysis
  • Implement safeguards such as human-in-the-loop workflows, schema validation, and output controls
  • Ensure systems are secure against prompt injection, data leakage, and unauthorized access
  • Collaborate with leadership and cross-functional teams to define and execute AI initiatives
  • Provide hands-on technical guidance, mentoring, and code reviews
  • Promote iterative delivery with frequent releases and continuous feedback loops
Required Qualifications
  • Proven experience building and deploying production-grade LLM or generative AI systems
  • Strong expertise in prompt design, orchestration, and model tradeoffs
  • Experience developing evaluation frameworks for AI outputs and validating quality
  • Solid background in distributed systems and production software engineering
  • Experience with CI/CD pipelines, release management, and operational ownership
  • Demonstrated ability to define technical standards and influence architecture decisions
  • Experience with cloud-native systems, APIs, and event-driven architectures (Azure or similar)
  • Experience integrating AI solutions with enterprise data and security requirements
  • Bachelor's degree in a technical field or equivalent practical experience
Preferred Qualifications
  • Experience with advanced RAG pipelines and agent-based AI systems in production
  • Familiarity with cloud AI services and modern infrastructure tooling
  • Experience with Python-based AI frameworks and data pipelines
  • Experience with containerization and deploying AI workloads
  • Knowledge of responsible AI practices and governance
  • Domain experience in areas such as product data, ERP, ecommerce, or analytics platforms

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