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Entry Level Generative Ai Engineer Jobs in Raleigh, NC

LexisNexis

Sr Data Scientist II

Raleigh, NC · On-site

... Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI systems, combined with strong software-engineering fundamentals and demonstrated ownership of production applications. This role ...

Pwc

US Tech - AI Engineering Senior Associate

Raleigh, NC · On-site

$55K - $187K/yr

... and validate Generative AI agents and data pipelines, promoting reliability, scalability, and ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

Bandwidth

NetSuite Application Developer

Raleigh, NC · On-site

We are seeking an innovative Internal Applications Engineer III specializing in NetSuite and ... Identify opportunities for automation within existing processes, leveraging generative AI and low ...

Bandwidth

NetSuite Application Developer

Raleigh, NC · On-site

We are seeking an innovative Internal Applications Engineer III specializing in NetSuite and ... Identify opportunities for automation within existing processes, leveraging generative AI and low ...

DPR Construction

MLOps Engineer

Raleigh, NC

MLOps Engineer DPR is a leading construction company committed to delivering high-quality ... Exposure to managing and monitoring ML workloads that support generative AI or advanced analytics ...

Horizon Research Inc.

Be Seen First

Entry-Level Research Associate

Chapel Hill, NC · On-site

Mathematics, science, computer science, or engineering teaching experience in formal or informal ... generative AI tools such as Copilot, ChatGPT, Claude, and Gemini ethically and productively ...

Horizon Research Inc.

Be Seen First

Entry-Level Research Associate

Chapel Hill, NC · On-site

Mathematics, science, computer science, or engineering teaching experience in formal or informal ... generative AI tools such as Copilot, ChatGPT, Claude, and Gemini ethically and productively ...

Litera

Associate, Legal Knowledge Engineering

Raleigh, NC · Hybrid

$115K - $133K/yr

Optimized generative AI experiences. Experiment with and validate LLM-driven prompting within ... Advise engineering teams on how legal processes work in practice, preventing design choices that ...

Bandwidth

Internal Applications Engineer

Raleigh, NC · On-site

We are seeking an innovative and AI-savvy Internal Applications Engineer to help build and maintain ... Identify opportunities for automation within existing processes, leveraging generative AI, workflow ...

Bandwidth

Internal Applications Engineer

Raleigh, NC · On-site

We are seeking an innovative and AI-savvy Internal Applications Engineer to help build and maintain ... Identify opportunities for automation within existing processes, leveraging generative AI, workflow ...

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All JobsGenerative Ai Engineer JobsEntry Level Generative Ai Engineer JobsEntry Level Generative Ai Engineer Jobs in Raleigh, NC

Entry Level Generative Ai Engineer information

See Raleigh, NC salary details

$29.2K

$67.4K

$114.7K

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

As of Aug 18, 2026, the average yearly pay for entry level generative ai engineer in Raleigh, NC is $67,425.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,100.00 and $76,300.00 per year, depending on experience, location, and employer.

What is an entry level generative AI engineer?

Entry level generative AI engineers are professionals who work with artificial intelligence technologies focused on creating new content such as images, text, audio, or code. They typically assist in developing, training, and fine-tuning machine learning models like GPT or GANs under the supervision of senior engineers. These roles usually require a strong foundation in programming, mathematics, and machine learning concepts, but may not demand extensive industry experience. Tasks often include data preprocessing, model evaluation, and contributing to research or product development involving generative AI.

What are the key skills and qualifications needed to thrive as an entry level generative AI engineer?

To thrive as an Entry Level Generative AI Engineer, you need a solid background in computer science, mathematics, and machine learning fundamentals, typically supported by a relevant degree or coursework. Familiarity with Python, deep learning frameworks like TensorFlow or PyTorch, and version control systems such as Git is important, along with any foundational certifications in AI or data science. Strong problem-solving ability, curiosity, and effective teamwork skills will help you stand out in this collaborative and innovative field. These skills and qualities are crucial for developing, testing, and improving generative AI models in a rapidly evolving technical landscape.

What are common challenges faced by entry level generative AI engineers, and how can they be addressed?

Entry level Generative AI Engineers often encounter challenges such as mastering complex machine learning frameworks, understanding the nuances of training large models, and keeping up with rapidly evolving research. Collaborating closely with more experienced team members through code reviews and pair programming can accelerate learning. It's also helpful to engage in continuous education through online courses and participate in team discussions to stay updated on the latest advancements and best practices in the field.

What is the difference between Entry Level Generative Ai Engineer vs Data Scientist?

AspectEntry Level Generative Ai EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related field; basic knowledge of machine learning and programmingBachelor's or higher in CS, Statistics, or related; knowledge of data analysis and modeling
Work EnvironmentTech companies, AI startups, research labs focusing on AI model developmentVarious industries including finance, healthcare, marketing; analyzing data to inform decisions
Employer & Industry UsagePrimarily in AI and tech sectors developing generative modelsAcross multiple sectors using data to solve business problems

While both roles require a background in data and programming, Entry Level Generative Ai Engineers focus on developing AI models like generative adversarial networks, whereas Data Scientists analyze data to generate insights. The former is more specialized in AI model creation, while the latter covers broader data analysis tasks.

What are the most commonly searched types of Generative Ai Engineer jobs in Raleigh, NC?

The most popular types of Generative Ai Engineer jobs in Raleigh, NC are:

What are popular job titles related to Entry Level Generative Ai Engineer jobs in Raleigh, NC?

For Entry Level Generative Ai Engineer jobs in Raleigh, NC, the most frequently searched job titles are:

What job categories do people searching Entry Level Generative Ai Engineer jobs in Raleigh, NC look for?

The top searched job categories for Entry Level Generative Ai Engineer jobs in Raleigh, NC are:

Entry Level Generative Ai Engineer jobs near you
Infographic showing various Entry Level Generative Ai Engineer job openings in Raleigh, NC as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution, with an average salary of $67,425 per year, or $32.4 per hour.

Sr Data Scientist II

Reed Elsevier Technology Services

Raleigh, NC • On-site

Other

Posted 4 days ago

Job description

Are you excited about shaping the next generation of AI-powered legal technology through generative AI, retrieval systems, and production-grade machine learning?
Do you enjoy building reliable, scalable applications that transform complex AI capabilities into impactful customer solutions?
About our Team
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (, a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today's top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (;br>
About the Role
We are looking for a Senior Data Scientist II with deep expertise in Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI systems, combined with strong software-engineering fundamentals and demonstrated ownership of production applications. This role will focus on improving LLM-powered drafting and retrieval solutions through advanced search, embeddings, reranking, evaluation, and production-grade ML components.
The successful candidate must be able to independently design, refactor, test, review, deploy, and support clean, reliable Python applications. This includes separating agent responsibilities, designing for failure, applying sound algorithmic reasoning, and establishing appropriate logging, monitoring, testing, and operational controls.
The ideal candidate has advanced Python proficiency, experience with OpenSearch or Solr, success working in monorepo environments, and a strong record of cross-functional delivery.
Key Responsibilities
  • Architect modular agentic applications with clear separation among retrieval, prompt construction, model invocation, tool execution, state and history management, orchestration, validation, and response formatting.
  • Independently refactor complex or legacy Python code to improve correctness, readability, modularity, extensibility, testability, and runtime performance.
  • Own production readiness for AI components, including input validation, exception handling, timeout management, retries with backoff, fallback behavior, configuration management, and secure handling of credentials.
  • Establish observability for LLM and retrieval workflows through structured logging, metrics, distributed tracing, alerting, and actionable error reporting.
  • Design clear interfaces and data contracts between retrieval, orchestration, model, and downstream application components.
  • Write comprehensive unit, integration, regression, and end-to-end tests, including tests for failure modes, malformed model responses, empty retrieval results, and unavailable dependencies.
  • Review Python and agentic application code, identify architectural and operational risks, and provide actionable feedback aligned with production engineering standards.
  • Diagnose and optimize latency, memory usage, retrieval performance, token consumption, model cost, and application scalability.
  • Apply appropriate data structures, algorithms, and computational-complexity analysis when designing and optimizing solutions.
  • Participate in production deployments, incident investigation, root-cause analysis, remediation, and continuous reliability improvements.

Required Qualifications
  • Advanced Python proficiency demonstrated through independently designing, implementing, debugging, testing, reviewing, and refactoring production applications.
  • Strong command of Python fundamentals, standard data structures, common algorithms, object-oriented and functional design principles, type annotations, and time and space complexity analysis.
  • Demonstrated ability to transform prototype or experimental code into modular, maintainable, observable, and production-ready systems.
  • Strong understanding of software design principles, including separation of concerns, dependency injection, interface design, configuration management, and effective abstraction.
  • Experience implementing automated unit, integration, regression, and end-to-end testing using tools such as pytest, including appropriate mocking of external services.
  • Experience designing resilient distributed applications that account for timeouts, retries, rate limits, partial failures, malformed responses, idempotency, and graceful degradation.
  • Experience with production observability, including structured logging, metrics, tracing, alerting, and incident troubleshooting.
  • Demonstrated ability to conduct rigorous code reviews and identify correctness, maintainability, performance, security, testing, and operational risks.
  • Experience taking technical ownership of applications across their lifecycle, from design and experimentation through deployment, monitoring, incident response, and ongoing improvement.
  • Strong understanding of production LLM concerns, including structured output validation, context management, model and tool failures, prompt versioning, token and cost controls, security, and evaluation.

Preferred Qualifications
  • Experience with Python quality tooling such as pytest, ruff, mypy, profiling tools, and automated CI quality gates.
  • Experience defining typed schemas and validating LLM inputs and outputs using tools such as Pydantic.
  • Experience building evaluation frameworks for agentic systems, including task-completion, retrieval-quality, groundedness, hallucination, latency, reliability, and cost metrics.
  • Experience implementing model fallbacks, tool-use controls, guardrails, human-in-the-loop workflows, and auditability for AI applications.
  • Experience supporting production services and participating in incident response, root-cause analysis, and post-incident remediation.

The successful candidate will:
  • Demonstrate senior-level Python proficiency and sound computer-science fundamentals.
  • Treat correctness, maintainability, testing, resilience, security, and observability as core design requirements.
  • Recognize architectural issues and improve code beyond simply making it functional.
  • Independently review and refactor complex agentic application code.
  • Make clear engineering tradeoffs involving quality, latency, scalability, reliability, and cost.
  • Take end-to-end ownership from experimentation through production deployment and operational support.
  • Communicate technical decisions and code-review feedback clearly and constructively.
  • Combine strong LLM and retrieval expertise with disciplined software-engineering practices.

Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
Working Pattern
Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
About the Business
LexisNexis Legal & Professional provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision-making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis and Nexis services. #AIFluent
U.S. National Base Pay Range: $104,900 - $174,700. Geographic differentials may apply in some locations to better reflect local market rates.If performed in Illinois, the base pay range is $110,100 - $183,500.If performed in Chicago, IL, the base pay range is $115,400 - $192,200.This job is eligible for an annual incentive bonus.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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