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Entry Level Generative Ai Engineer Jobs (NOW HIRING)

Nimble Gravity

AI Engineer

AI Engineer - Generative AI & Agentic Systems We're looking for an exceptional AI Engineer to join our growing Data & AI practice and help build the next generation of AI-powered solutions. This role ...

Expleo

AI Engineer

Los Angeles, CA

Missions Nous recherchons un(e) AI Engineer passionne(e) par les technologies d'Intelligence ... Concevoir et developper des solutions basees sur les technologies Generative AI : * RAG (Retrieval ...

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

As of Jun 1, 2026, the average yearly pay for entry level generative ai engineer in the United States is $69,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,500.00 and $78,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level Generative AI Engineer, and why are they important?

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 are entry level generative AI engineers?

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 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.

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Entry Level Generative Ai Engineer jobs near you
AI Engineer

AI Engineer

Nimble Gravity

Remote

Full-time

Posted 25 days ago

Job description

AI Engineer - Generative AI & Agentic Systems
We're looking for an exceptional AI Engineer to join our growing Data & AI practice and help build the next generation of AI-powered solutions.
This role is ideal for someone who loves working at the edge of what's possible with LLMs, RAG, agents, semantic search, multimodal AI, and intelligent automation - and who wants to turn cutting-edge ideas into real, production-ready systems for clients across industries.
This position is open to candidates based in LATAM or the United States.
What You'll Do
  • Design, build, and deploy advanced AI solutions using LLMs, RAG, embeddings, vector databases, semantic search, and agentic architectures.
  • Develop intelligent systems that can process documents, retrieve knowledge, generate content, automate workflows, and transform unstructured data into actionable insights.
  • Work with modern GenAI and AI engineering tools such as LangChain, LangGraph, Hugging Face, PyTorch, MCP, multi-agent frameworks, and cloud-native AI services.
  • Build scalable, reliable AI applications across Azure, AWS, Databricks, or similar cloud/data platforms.
  • Experiment with and evaluate foundation models, prompts, retrieval strategies, orchestration patterns, and performance optimization techniques.
  • Collaborate closely with engineering, product, data, and business teams to define the right technical approach and deliver high-impact AI solutions.
  • Stay plugged into the fast-moving GenAI ecosystem and bring new ideas, tools, and prototypes into real-world applications.

What You Bring
  • Strong experience in AI, machine learning, data science, or AI engineering, with hands-on exposure to production-scale solutions.
  • Deep interest and practical experience with Generative AI, including LLMs, RAG, vector search, embeddings, agents, and AI workflow orchestration.
  • Strong Python skills and experience with modern ML/AI frameworks and libraries.
  • Experience building on cloud or data platforms such as Azure, AWS, GCP, Databricks, or similar.
  • A solid foundation in data science, experimentation, statistical thinking, or applied machine learning.
  • Ability to work with structured and unstructured data, including text, documents, images, emails, spreadsheets, or other complex data formats.
  • A builder mindset: curious, hands-on, adaptable, and excited to work with emerging technologies.
  • Strong communication skills and the ability to partner with technical and non-technical stakeholders.

Nice to Have
  • Advanced degree in Computer Science, Data Science, AI, Machine Learning, or a related technical field.
  • Experience with MCP, multi-agent systems, fine-tuning, LLM evaluation frameworks, multimodal AI, or production ML observability.
  • Prior consulting, client-facing, or cross-functional delivery experience.

About Nimble Gravity
Nimble Gravity is a team of outdoor enthusiasts, adrenaline seekers, and experienced growth hackers. We love solving hard problems and believe the right data can transform and propel growth for any organization.
Nimble Gravity is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, state, or local law. Nimble Gravity considers all qualified applicants.

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