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Generative Ai Analyst 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 ... Lead incident response and root cause analysis, implementing long-term fixes * Develop evaluation ...

Ofinno

Generative AI Architect

Reston, VA · On-site

$65.75 - $86.50/hr

Generative AI Architect About Ofinno: Ofinno is a leading research and development lab ... Monitor and analyze the performance of deployed systems, applying iterative improvements based on ...

Ofinno

Generative AI Architect

Reston, VA · On-site

$65.50 - $86.25/hr

Generative AI Architect About Ofinno: Ofinno is a leading research and development lab ... Monitor and analyze the performance of deployed systems, applying iterative improvements based on ...

Precision AQ

Senior Data and AI Analyst

New York, NY · On-site

$94K - $118K/yr

Senior Data and AI Analyst, Investor Relations Position Summary The Senior Data and AI Analyst ... Conduct market research on emerging trends in Generative AI, digital platforms, and healthcare ...

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Generative Ai Analyst information

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$49K

$88.6K

$123.5K

How much do generative ai analyst jobs pay per year?

As of Jun 10, 2026, the average yearly pay for generative ai analyst in the United States is $88,569.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,000.00 and $99,500.00 per year, depending on experience, location, and employer.

What is the difference between Generative Ai Analyst vs Data Scientist?

AspectGenerative Ai AnalystData Scientist
Required CredentialsBachelor's in CS, AI, or related fields; certifications in AI/MLBachelor's/Master's in CS, Statistics, or related fields; advanced certifications
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Employer & Industry UsageFocus on developing and refining generative AI modelsAnalyze data, build predictive models, derive insights
Common Search & Comparison IntentUnderstanding roles in AI developmentData analysis and modeling skills

While both roles require strong technical skills and knowledge of AI and data analysis, a Generative Ai Analyst specializes in creating and optimizing generative AI models, whereas a Data Scientist focuses on analyzing data to inform business decisions. The roles often overlap but differ in their primary focus and application within organizations.

What are the key skills and qualifications needed to thrive as a Generative AI Analyst, and why are they important?

To thrive as a Generative AI Analyst, you need a solid background in data science, machine learning, and statistics, often supported by a degree in computer science or a related field. Familiarity with tools and frameworks such as Python, TensorFlow, PyTorch, and experience with large language models or generative adversarial networks (GANs) is typically required. Strong analytical thinking, creativity, and effective communication skills help you interpret complex data and present insights to stakeholders. These skills and qualities are crucial for developing innovative AI solutions, solving business challenges, and driving impactful results.

How does a Generative AI Analyst typically collaborate with data scientists and engineering teams?

A Generative AI Analyst frequently works alongside data scientists and engineering teams to interpret model outputs, assess data quality, and help translate business objectives into technical requirements. Collaboration usually involves regular meetings to review model performance, troubleshoot issues, and refine algorithms based on real-world feedback. Effective communication and a shared understanding of both AI concepts and business goals are essential, as the analyst often serves as a bridge between technical teams and stakeholders. This collaborative environment fosters continuous learning and innovation, making teamwork a core aspect of the role.

What is a Generative AI Analyst?

A Generative AI Analyst is a professional who specializes in analyzing, designing, and optimizing systems that use generative artificial intelligence models, such as large language models or image generators. Their work involves understanding how these AI models are developed, deployed, and utilized across various applications. They assess data quality, monitor model outputs, evaluate performance, and help improve the effectiveness and ethical use of generative AI technologies. Generative AI Analysts may also provide insights to organizations on best practices, risk management, and innovation opportunities related to AI. Their expertise bridges the gap between data science, AI development, and business strategy.
More about Generative Ai Analyst jobs
What cities are hiring for Generative Ai Analyst jobs? Cities with the most Generative Ai Analyst job openings:
What states have the most Generative Ai Analyst jobs? States with the most job openings for Generative Ai Analyst jobs include:
What job categories do people searching Generative Ai Analyst jobs look for? The top searched job categories for Generative Ai Analyst jobs are:
Generative Ai Analyst jobs near you
Infographic showing various Generative Ai Analyst job openings in the United States as of June 2026, with employment types broken down into 84% Full Time, 6% Part Time, and 10% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $88,569 per year, or $42.6 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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