... including Generative AI, Large Language Models (LLMs), machine learning, and Natural Language ... • Any entry-level certification in AI Engineering, DevOps or Cloud Engineering Company : The ...
... including Generative AI, Large Language Models (LLMs), machine learning, and Natural Language ... • Any entry-level certification in AI Engineering, DevOps or Cloud Engineering Company : The ...
Generative Ai Entry Level information
See Reston, VA salary details
$16.01 - $17.30
4% of jobs
$17.30 - $18.60
8% of jobs
$18.60 - $19.89
11% of jobs
$20.15 is the 25th percentile. Wages below this are outliers.
$19.89 - $21.19
9% of jobs
$21.19 - $22.48
12% of jobs
The median wage is $23.13 / hr.
$22.48 - $23.78
12% of jobs
$23.78 - $25.08
14% of jobs
$25.70 is the 75th percentile. Wages above this are outliers.
$25.08 - $26.37
12% of jobs
$26.37 - $27.67
8% of jobs
$27.67 - $28.96
5% of jobs
$28.96 - $30.26
5% of jobs
$16
$23
$30
How much do generative ai entry level jobs pay per hour?
As of May 30, 2026, the average hourly pay for generative ai entry level in Reston, VA is $23.38, according to ZipRecruiter salary data. Most workers in this role earn between $20.24 and $25.77 per hour, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as a Generative AI Entry Level professional, and why are they important?
To thrive as a Generative AI Entry Level professional, you need a basic understanding of machine learning concepts, Python programming, and a relevant degree such as computer science or data science. Familiarity with tools like TensorFlow, PyTorch, and version control systems, as well as exposure to cloud platforms, is typically expected. Strong problem-solving abilities, eagerness to learn, and the ability to communicate technical information clearly help individuals stand out in this role. These skills and qualities are crucial to effectively contribute to AI projects, adapt to rapidly evolving technologies, and collaborate within technical teams.
What types of projects do entry-level professionals in Generative AI typically work on, and how do they contribute to larger team goals?
As an entry-level professional in Generative AI, you may work on tasks such as data preprocessing, model training, and evaluating the performance of generative models. You'll often collaborate with researchers, data scientists, and software engineers to support more complex projects, such as developing new text or image generation tools. Your contributions help ensure data quality and model accuracy, and you may also assist in documenting results and preparing presentations. This collaborative environment allows you to learn from experienced colleagues while building foundational skills for future growth.
What does a Generative AI Entry Level professional do?
A Generative AI Entry Level professional assists in developing, training, and testing artificial intelligence models that can create new content, such as text, images, or audio. They often work with large datasets, help fine-tune AI models, and support more senior engineers in implementing machine learning algorithms. Typical tasks include data preprocessing, running experiments, evaluating model outputs, and documenting results. This role is ideal for those new to the AI field and looking to gain hands-on experience in generative technologies.
What are the most commonly searched types of Generative Ai jobs in Reston, VA? The most popular types of Generative Ai jobs in Reston, VA are:
What job categories do people searching Generative Ai Entry Level jobs in Reston, VA look for? The top searched job categories for Generative Ai Entry Level jobs in Reston, VA are:
What cities near Reston, VA are hiring for Generative Ai Entry Level jobs? Cities near Reston, VA with the most Generative Ai Entry Level job openings:
Full-time
Posted 4 days ago
Job description
Job Summary:
The World Bank Group (WBG) provides a unique opportunity to help countries solve their greatest development challenges. The AI Solutions Analyst role involves building AI-powered solutions and contributing to the design, development, and operation of next-generation AI-enabled platforms and services to address real-world development challenges.
Responsibilities:
• Build AI-powered solutions leveraging hyper-scaler foundational AI services (e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI managed AI/ML platforms)
• Apply foundation models and agentic AI patterns to solve real-world development challenges across World Bank business domains
• Implement sandboxed environments to safely test and evaluate AI agents before production deployment
• Ensure agents operate within controlled boundaries, including restricted tool access, data scope, and execution limits
• Contribute to building AI agent frameworks, SDKs, and reusable components enabling rapid solution development
• Assist in the design, fine-tuning, and evaluation of machine learning models & SLMs via MLOps.
• Support experimentation workflows for context engineering, model evaluation, and iterative improvements
• Contribute to responsible AI practices, including safety, explainability, and governance
• Help operationalize AI solutions using CI/CD deployment pipelines and runtime environments
• Leverage agent orchestration and agent harness patterns to safely deploy AI use cases in production
• Participate in designing APIs and services that expose AI capabilities to business applications
• Support observability through AI system monitoring, feedback loops, and performance insights
• Work under guidance of senior engineers while progressively building independence and technical depth
• Gain exposure to enterprise-scale AI applications and real-world development impact
• Support development, testing, and deployment of AI/ML models and platform components.
• Assist in building and enhancing internal AI platforms and self-service capabilities
• Implement and support monitoring, observability, and operational reliability practices
• Collaborate with cross-functional teams and contribute to agile development processes
• Enable delivery of self-service production-grade AI/ML solutions through robust engineering practices
• Develop Agentic AI Core & Foundational guard rails to enable AI at scale.
• Experiment with AI industry trends and assess feasibility, & viability.
• Leverage modern Agent Harness to safely deploy Agentic AI use cases in production.
• Build foundational expertise across AI, DevSecOps, and platform engineering domains
Qualifications:
Required:
• Bachelor’s or Master’s degree with 2 years of experience or equivalent combination of education and experience (for example, in the IT field: Bachelor’s Degree with a minimum of 1 year of related work experience)
• Demonstrated exposure to AI/ML, cloud computing, or DevOps practices, with a clear interest in building scalable, production-ready systems
• Proficiency in at least one programming language (e.g., Python, Node.js, Go or similar), with a solid foundation in software engineering, cloud architecture, and modern development practices (Git, APIs)
• Working knowledge of cloud platforms (Azure, AWS, GCP), including automation, CI/CD pipelines, containerization, and infrastructure-as-code
• Understanding of API design, API Management (APIM), API gateways, and API security, including OAuth 2.0, OpenID Connect (OIDC), and identity solutions such as Azure Entra ID
• Familiarity with AI Gateway concepts, token consumption models, and observability tools for troubleshooting and performance monitoring
• Demonstrated interest and working knowledge in Artificial Intelligence, including Generative AI, Large Language Models (LLMs), machine learning, and Natural Language Processing (NLP)
• Experience or exposure to automation, platform engineering, and scalable system design, with awareness of microservices architecture and deep learning concepts
• Strong analytical, problem-solving, and critical thinking skills, with the ability to quickly learn and adapt in fast-paced technical environments
• Effective communication and collaboration skills, with a curiosity-driven mindset and commitment to continuous learning and innovation
Preferred:
• Microsoft Azure Fundamentals (AZ-900) or AWS Certified Cloud Practitioner
• Azure AI Fundamentals (AI-900) or equivalent
• Kubernetes or Docker introductory certifications
• Any entry-level certification in AI Engineering, DevOps or Cloud Engineering
Company:
The World Bank is a vital source of financial and technical assistance to developing countries around the world. Founded in 1994, the company is headquartered in Washington, USA, with a team of 10001+ employees. The company is currently Late Stage.
The World Bank Group (WBG) provides a unique opportunity to help countries solve their greatest development challenges. The AI Solutions Analyst role involves building AI-powered solutions and contributing to the design, development, and operation of next-generation AI-enabled platforms and services to address real-world development challenges.
Responsibilities:
• Build AI-powered solutions leveraging hyper-scaler foundational AI services (e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI managed AI/ML platforms)
• Apply foundation models and agentic AI patterns to solve real-world development challenges across World Bank business domains
• Implement sandboxed environments to safely test and evaluate AI agents before production deployment
• Ensure agents operate within controlled boundaries, including restricted tool access, data scope, and execution limits
• Contribute to building AI agent frameworks, SDKs, and reusable components enabling rapid solution development
• Assist in the design, fine-tuning, and evaluation of machine learning models & SLMs via MLOps.
• Support experimentation workflows for context engineering, model evaluation, and iterative improvements
• Contribute to responsible AI practices, including safety, explainability, and governance
• Help operationalize AI solutions using CI/CD deployment pipelines and runtime environments
• Leverage agent orchestration and agent harness patterns to safely deploy AI use cases in production
• Participate in designing APIs and services that expose AI capabilities to business applications
• Support observability through AI system monitoring, feedback loops, and performance insights
• Work under guidance of senior engineers while progressively building independence and technical depth
• Gain exposure to enterprise-scale AI applications and real-world development impact
• Support development, testing, and deployment of AI/ML models and platform components.
• Assist in building and enhancing internal AI platforms and self-service capabilities
• Implement and support monitoring, observability, and operational reliability practices
• Collaborate with cross-functional teams and contribute to agile development processes
• Enable delivery of self-service production-grade AI/ML solutions through robust engineering practices
• Develop Agentic AI Core & Foundational guard rails to enable AI at scale.
• Experiment with AI industry trends and assess feasibility, & viability.
• Leverage modern Agent Harness to safely deploy Agentic AI use cases in production.
• Build foundational expertise across AI, DevSecOps, and platform engineering domains
Qualifications:
Required:
• Bachelor’s or Master’s degree with 2 years of experience or equivalent combination of education and experience (for example, in the IT field: Bachelor’s Degree with a minimum of 1 year of related work experience)
• Demonstrated exposure to AI/ML, cloud computing, or DevOps practices, with a clear interest in building scalable, production-ready systems
• Proficiency in at least one programming language (e.g., Python, Node.js, Go or similar), with a solid foundation in software engineering, cloud architecture, and modern development practices (Git, APIs)
• Working knowledge of cloud platforms (Azure, AWS, GCP), including automation, CI/CD pipelines, containerization, and infrastructure-as-code
• Understanding of API design, API Management (APIM), API gateways, and API security, including OAuth 2.0, OpenID Connect (OIDC), and identity solutions such as Azure Entra ID
• Familiarity with AI Gateway concepts, token consumption models, and observability tools for troubleshooting and performance monitoring
• Demonstrated interest and working knowledge in Artificial Intelligence, including Generative AI, Large Language Models (LLMs), machine learning, and Natural Language Processing (NLP)
• Experience or exposure to automation, platform engineering, and scalable system design, with awareness of microservices architecture and deep learning concepts
• Strong analytical, problem-solving, and critical thinking skills, with the ability to quickly learn and adapt in fast-paced technical environments
• Effective communication and collaboration skills, with a curiosity-driven mindset and commitment to continuous learning and innovation
Preferred:
• Microsoft Azure Fundamentals (AZ-900) or AWS Certified Cloud Practitioner
• Azure AI Fundamentals (AI-900) or equivalent
• Kubernetes or Docker introductory certifications
• Any entry-level certification in AI Engineering, DevOps or Cloud Engineering
Company:
The World Bank is a vital source of financial and technical assistance to developing countries around the world. Founded in 1994, the company is headquartered in Washington, USA, with a team of 10001+ employees. The company is currently Late Stage.
About World Bank
Sourced by ZipRecruiter
Industry
International trade financing
Company size
1,001 - 5,000 Employees
Headquarters location
Washington, DC, US
Year founded
1994