· NLP & LLM Technologies
NLP & LLM Technologies

NLP & LLM Technologies

Natural Language Processing and Large Language Model technologies enable building AI applications that understand and generate human language, representing one of the fastest-growing areas in software engineering. Langchain dominates LLM application development with >20% prevalence in LLM/AI Application Development positions, providing frameworks for chaining LLM operations and building AI agents. LLMs themselves show specialized adoption: OpenAI APIs, Claude, and GPT models appear in >5-10% of LLM/AI developer roles. Hugging Face and Transformers serve model access and fine-tuning needs (>5% prevalence), while LlamaIndex specializes in retrieval-augmented generation (>15% in LLM developer roles). Traditional NLP frameworks maintain presence: general Natural Language Processing skills appear in >10% of Machine Learning Engineering and >5% of Data Science roles. Entry-level accessibility is moderate for Langchain (>35% in entry-level LLM developer positions), OpenAI (>10%), and foundational NLP concepts (>10% in ML roles). The field reflects rapid evolution from traditional NLP toward LLM-powered applications, with new frameworks emerging for vector databases, retrieval-augmented generation, and agent architectures. These technologies are central to the emerging LLM/AI application developer specialization.

LLM Application Frameworks

Frameworks for building applications powered by Large Language Models, enabling chaining, retrieval-augmented generation, and agent orchestration. Langchain leads application development, LlamaIndex specializes in RAG patterns, and Hugging Face provides model access. Strong entry-level opportunities for Langchain in the emerging LLM developer space.

Langchain

High Demand
Rank: #1
Entry-Level: High
Leading LLM application framework in LLM/AI Application Development (>20%). Strong entry-level demand with >35% prevalence in LLM developer roles. Chains and agents for LLMs. Used for building LLM-powered applications, chaining multiple LLM calls, retrieval-augmented generation, conversational AI agents, document question-answering systems, integrating LLMs with external tools and APIs, and orchestrating complex LLM workflows.

LlamaIndex

Moderate Demand
Rank: #2
Entry-Level: Moderate
RAG-focused framework in LLM/AI Application Development (>15%). Moderate entry-level demand with >10% prevalence. Data framework for LLMs. Used for connecting LLMs to external data sources, building retrieval-augmented generation systems, indexing and querying documents, semantic search over proprietary data, creating context-aware LLM applications, and ingesting structured/unstructured data for LLM access.

Hugging Face Transformers

Low Demand
Rank: #3
Entry-Level: Low
Hugging Face's transformers library in LLM/AI Application Development (>5%), Machine Learning Engineering (>5%), MLOps, and Data Science. Lower entry-level accessibility. Model repository and transformer library. Used for accessing pre-trained models, fine-tuning transformers (BERT, GPT, T5), implementing state-of-art NLP models, transfer learning, sharing and deploying models, text classification and generation, named entity recognition, and leveraging community-contributed models for various NLP tasks.

LLM Providers & Models

Large Language Model APIs and services providing foundational AI capabilities. OpenAI leads with GPT models and APIs, while Claude represents Anthropic's offerings. These models power AI applications through API integration with moderate entry-level accessibility in LLM development roles.

OpenAI

Moderate Demand
Rank: #1
Entry-Level: Moderate
OpenAI's LLM APIs and services in LLM/AI Application Development (>10%). Moderate entry-level demand with >10% prevalence. GPT model provider. Used for integrating GPT-3.5/GPT-4 models, building conversational AI, text generation and completion, embeddings for semantic search, function calling capabilities, DALL-E image generation, and leveraging state-of-art language models via API.

Claude

Low Demand
Rank: #2
Entry-Level: Low
Anthropic's LLM in LLM/AI Application Development (>5%). Lower entry-level presence. Constitutional AI approach. Used for building AI assistants, long-context applications (100K+ tokens), safer AI applications with reduced harmful outputs, conversational agents, document analysis and summarization, and applications requiring extended context windows beyond other LLMs.

GPT models

Low Demand
Rank: #3
Entry-Level: Low
OpenAI's GPT model family with implicit presence across LLM development. Often implied with OpenAI API usage. Used for text generation, conversational AI, content creation, code generation, reasoning tasks, few-shot learning, and powering applications with state-of-art language understanding and generation capabilities.

Traditional NLP Tools & Concepts

Natural Language Processing frameworks and techniques predating the LLM era, still relevant for specialized NLP tasks, linguistic analysis, and understanding NLP fundamentals. These skills appear across ML engineering and data science with moderate entry-level accessibility.

Natural Language Processing

Moderate Demand
Rank: #1
Entry-Level: Moderate
General NLP skills and concepts in Machine Learning Engineering (>10%), LLM/AI Application Development (>10%), Data Science (>5%), and MLOps. Moderate entry-level demand with >10% prevalence. Broad NLP domain. Used for text preprocessing and tokenization, sentiment analysis, named entity recognition, text classification, language understanding tasks, feature extraction from text, and foundational understanding of linguistic and statistical NLP methods.

NLP

Moderate Demand
Rank: #2
Entry-Level: Moderate
Abbreviation for Natural Language Processing in Machine Learning Engineering (>10%), Data Science (>10%), LLM/AI Application Development (>10%), and AI roles. Same prevalence and use cases as full 'Natural Language Processing': text analysis, language models, information extraction, machine translation, speech recognition integration, and computational linguistics applications.

BERT

Low Demand
Rank: #3
Entry-Level: Low
Bidirectional transformer model with limited explicit presence (<5% overall prevalence). Foundational but often implied. Pre-training technique. Used for transfer learning in NLP, text classification with fine-tuning, question answering systems, semantic similarity, masked language modeling, understanding context in both directions, and serving as foundation for many modern NLP applications.

spaCy

Low Demand
Rank: #4
Entry-Level: Low
Industrial-strength NLP library with minimal explicit presence (<5% prevalence). Python NLP library. Used for named entity recognition, part-of-speech tagging, dependency parsing, linguistic annotations, production NLP pipelines, text preprocessing, and efficient NLP operations without deep learning overhead for traditional NLP tasks.