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Entry Level Generative Ai Engineer Jobs in Vienna, VA

Merican

AWS AI Engineer

Herndon, VA · On-site

Onsite About the Role We are seeking a highly skilled AWS AI Engineer with strong hands-on experience in Kubernetes, EKS, and Generative AI systems. The ideal candidate will have deep expertise in ...

OpenDataJobs

AI Engineer Role

Washington, DC · On-site

The work AI Engineers build and operate systems that turn data, documents, models, and AI services ... generative AI, RAG, agents, computer vision, AI platforms, machine learning operations (MLOps ...

OpenDataJobs

AI Engineer Role

Washington, DC · On-site

The work AI Engineers build and operate systems that turn data, documents, models, and AI services ... generative AI, RAG, agents, computer vision, AI platforms, machine learning operations (MLOps ...

Booz Allen Hamilton

AI/ML Engineer

Mclean, VA

$55K - $126K/yr

AI/ML Engineer The Opportunity: As a programmer, you know that machine learning is critical to ... Experience working with LLMs, generative AI systems, or agentic frameworks * Knowledge of AI ...

Booz Allen Hamilton, Inc.

AI/ML Engineer

Mclean, VA · On-site

$55K - $126K/yr

AI/ML Engineer The Opportunity: As a programmer, you know that machine learning is critical to ... Experience working with LLMs, generative AI systems, or agentic frameworks * Knowledge of AI ...

Booz Allen Hamilton

AI/ML Engineer

Mclean, VA · On-site

$55K - $126K/yr

R0236420 AI/ML Engineer The Opportunity: As a programmer, you know that machine learning is ... Experience working with LLMs, generative AI systems, or agentic frameworks * Knowledge of AI ...

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Entry Level Generative Ai Engineer information

See Vienna, VA salary details

$30.1K

$69.6K

$118.4K

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

As of Sep 3, 2026, the average yearly pay for entry level generative ai engineer in Vienna, VA is $69,619.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,700.00 and $78,800.00 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.

Entry Level Generative Ai Engineer jobs near you
Infographic showing various Entry Level Generative Ai Engineer job openings in Vienna, VA as of August 2026, with employment types broken down into 4% Internship, 88% Full Time, 4% Part Time, and 4% Contract. Highlights an 85% In-person, 4% Hybrid, and 11% Remote job distribution, with an average salary of $69,619 per year, or $33.5 per hour.

Generative AI Engineer Role

OpenDataJobs

Washington, DC • On-site

Full-time

Posted 27 days ago

Job description

The work
Generative AI Engineers build production applications around foundation models and large language models. They turn model capabilities into tools for search, drafting, summarization, information extraction, multimodal work, and guided action, with the controls and evidence needed to understand how those tools behave.
The role concentrates on application and context engineering. Generative AI Engineers connect models to authoritative knowledge, tools, and workflows; design prompts and structured outputs; evaluate quality, grounding, safety, security, latency, and cost; and monitor the application after release. They treat fluent output as something to test, not proof that the system is correct.
What you'll build
• Grounded assistants and knowledge applications with retrieval-augmented generation (RAG), hybrid or vector search, citations, metadata filtering, and source-level access controls.
• Drafting, summarization, classification, extraction, and transformation services exposed through user interfaces or APIs.
• Agents and multistep workflows with defined tool schemas, constrained permissions, approval gates, memory boundaries, replay, and exception handling.
• Evaluation systems with curated test cases, task-specific rubrics, retrieval measures, grounding checks, safety and security tests, and regression thresholds.
• Operational pipelines for versioning prompts and configurations, comparing models, tracing execution, monitoring quality and cost, collecting feedback, and responding to incidents.
Who you are
You are an application engineer who can work with fast-moving model capabilities without chasing every new release. You choose architectures by evidence, make uncertainty visible, and separate a convincing demonstration from a dependable service.
You think in complete workflows: sources, context, models, tools, permissions, people, and failure paths. You collaborate with domain experts, data and software engineers, security and privacy specialists, and accountable owners to decide where generation helps and where deterministic methods or human judgment should remain in control.
What you bring
• A strong application-engineering foundation, including programming, APIs, testing, version control, service integration, and production debugging.
• Practical experience with foundation-model integration, prompt and context design, structured outputs, model selection, and failure analysis.
• Working knowledge of retrieval systems, embeddings, search, knowledge stores, document ingestion, and data-access controls.
• Evaluation discipline across answer quality, retrieval relevance, grounding, safety, security, latency, cost, and user outcomes.
• The judgment to constrain tools and agents, design human-review paths, document limitations, and respond when production behavior changes.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
Register for Generative AI Engineer Role
Click Apply below to register for Generative AI Engineer Role.
Requirements
What openings may require
An opening may emphasize enterprise search, document intelligence, multimodal applications, code generation, contact-center support, agent workflows, model adaptation, synthetic data, or evaluation and red-team engineering. Building or training a foundation model from scratch is not a universal requirement.
Specific openings may name a model provider, cloud platform, vector or search service, agent framework, observability stack, programming language, model-evaluation approach, content-safety service, or security and governance framework. OPEN Data Jobs will state which capabilities are required and which are preferred.
Benefits
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening

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