... NLP) • Experience or exposure to automation, platform engineering, and scalable system design ... Cloud Practitioner • Azure AI Fundamentals (AI-900) or equivalent • Kubernetes or Docker ...
... NLP) • Experience or exposure to automation, platform engineering, and scalable system design ... Cloud Practitioner • Azure AI Fundamentals (AI-900) or equivalent • Kubernetes or Docker ...
Entry Level Nlp Practitioner information
See salary details
$18.27 - $28.74
12% of jobs
$28.74 - $39.20
0% of jobs
$39.20 - $49.67
1% of jobs
$49.67 - $60.14
4% of jobs
$60.14 - $70.61
4% of jobs
$72.31 is the 25th percentile. Wages below this are outliers.
$70.61 - $81.08
24% of jobs
$81.08 - $91.54
4% of jobs
The median wage is $91.87 / hr.
$91.54 - $102.01
17% of jobs
$107.41 is the 75th percentile. Wages above this are outliers.
$102.01 - $112.48
17% of jobs
$112.48 - $122.95
3% of jobs
$122.95 - $133.41
14% of jobs
$18
$88
$133
How much do entry level nlp practitioner jobs pay per hour?
As of May 30, 2026, the average hourly pay for entry level nlp practitioner in the United States is $88.09, according to ZipRecruiter salary data. Most workers in this role earn between $76.92 and $110.10 per hour, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as an Entry Level NLP Practitioner, and why are they important?
To thrive as an Entry Level NLP Practitioner, you need a foundational understanding of natural language processing concepts, basic programming skills (often in Python), and a relevant degree in computer science or linguistics. Familiarity with NLP libraries such as NLTK, spaCy, or Hugging Face Transformers, and experience using machine learning frameworks like TensorFlow or PyTorch, are typically expected. Strong problem-solving abilities, attention to detail, and effective communication are standout soft skills in this role. These skills enable practitioners to accurately analyze language data, build useful NLP models, and collaborate effectively within technical teams.
What are some common challenges faced by entry-level NLP practitioners when working on real-world projects?
Entry-level NLP practitioners often encounter challenges such as dealing with messy or unstructured text data, selecting appropriate models for specific tasks, and balancing accuracy with computational efficiency. They may also need to collaborate closely with data engineers and domain experts to understand business requirements and to deploy NLP solutions effectively. Gaining familiarity with industry-standard tools and understanding how to interpret and communicate model results to non-technical stakeholders can also be demanding but are essential for success in this role.
What are entry level NLP practitioners?
Entry level NLP practitioners are professionals who have recently started working in the field of Natural Language Processing (NLP), typically with foundational knowledge of language models, text analysis, and machine learning techniques. They assist in developing, training, and testing NLP models under the guidance of more experienced team members. Their responsibilities often include cleaning and annotating text data, conducting basic data analysis, and implementing simple NLP algorithms. Entry level practitioners usually have a background in computer science, linguistics, or a related field, and are familiar with programming languages like Python.
What are the most commonly searched types of Nlp Practitioner jobs? The most popular types of Nlp Practitioner jobs are:
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