Top Rated on Upwork
15+ Years of Combined Expertise
100% Job Success on Upwork
Vetted Generative AI Engineers
Production-First AI Systems
Fast Onboarding
Hire NLP Engineers Who Build and Ship
Quantal AI helps businesses hire NLP developers in the US who can work on real language-processing projects, from text classification and entity recognition to LLM integration, RAG, and conversational AI. With 15+ years of combined expertise and a 100% Job Success on Upwork, our engineers can support a new NLP product, add language capabilities to an existing platform, or join your development team. Choose a dedicated, part-time, hourly, or project-based engagement based on your project's needs.
View Case Studies
100+
AI Developers
160+
Software Products Delivered
50+
AI Solutions
15+
Total Years of Experience
Our Trusted Clients
Meet Our Engineers
Meet Nagadia
3 Years of ExperienceSenior AI Engineer/Developer
- M.Sc. in AI AURO Univeristy - Surat, India
- B.Sc. in IT B.K.Birla - Mumbai, India
NLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics DevelopmentNLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics Development
Dev Chandan
3 Years of ExperienceSenior AI Engineer/Developer
- Post Graduate Program in Artificial Intelligence & Machine Learning, University of Texas at Austin (in collaboration with Great Lakes), 2025 — GPA: 3.72/4.0
- B.Tech in Artificial Intelligence, NMIMS Mukesh Patel School of Technology Management & Engineering (in collaboration with Virginia Tech), 2024 — CGPA: 3.93/4.0; Certificate of Merit
Generative AI
Large Language Models (GPT, Claude, Gemini, Llama, DeepSeek)
RAG
LangChain
Prompt Engineering
Function Calling
Fine-tuning (QLoRA)
Python
FastAPI
SQL
REST APIs
PyTorch
TensorFlow
Hugging Face
Kubernetes
Docker
Azure
GCP
GitHub Actions
Langfuse
LangSmith
Pinecone
Chroma
YOLOv8
OpenCV.
Sushant Upadhyay
3 Years of ExperienceSenior AI Engineer/Developer
- B.Sc. in Statistics Kishanchand Chellaram College - India
- M.Sc. in Big Data Analytics Jai Hind College - India
NLP
Machine Learning
Reinforcement learning
Artificial Intelligence
Big Data Analysis
Data Science
Back-end Developer
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL, Cassandra, MongoDB, GraphQL
Scala
Pyspark
TaraChand Vishnoi
5 Years of ExperienceAI Consultant
- Lovely Professional University
- Bachelor of Technology - BTech, Mechanical Engineering
NLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics Development
Diego Jimenez
14 Years of ExperienceUS Business Partner
- EAE Business School, Spain MBA - Master in International Business, International Business Minor - Design Thinking
- University of Leeds, United Kingdom Post Grad certification How to Choose the Right Problem to Solve
- Nebius Academy AI Agents and Automation Certification AI for Managers
- Politecnico de Suramerica, Colombia Applied Psychology of Sales
- AIEP Universidad Andres Bello, Chile Bachelor’s degree on Business Administration with an emphasis on Finances
Agentic AI
Development
AI Strategy
AI Transformation
Business Operations
Process Automation
AI Agents and Automation
Business Transformation
Revenue Analysis
Strategic Business Development
B2B Marketing Strategy
Growth Strategies
Sales Strategy
CRM Sales Management
International Business
Aamesh Gori
1.5 Years of ExperienceAI Engineer
- University of Mumbai - Bachelor's in Computer Engineering
AI Security
AI Governance
Agentic AI
AI Automation
Optical Character Recognition (OCR)
Retrieval-Augmented Generation (RAG)
Large Language Models (LLM)
Python
AWS
Azure
Kubernetes
Docker
Our Expertise
Core Capabilities of Our NLP Engineers
When you hire NLP engineers, you get support across LLMs, RAG, text processing, search, and conversational AI.
Text Classification and Sentiment Analysis
Classify emails, documents, reviews, support tickets, and other text based on the categories that matter to your business. Models can also be trained to recognize sentiment, intent, and industry-specific language.
Named Entity Recognition and Information Extraction
Extract valuable information from documents, emails, reports, and other unstructured sources. NLP pipelines can identify names, organizations, locations, dates, relationships, and custom entities.
LLM Integration and Fine-Tuning
Bring language models into existing applications or adapt them for a specific use case. Our engineers work with OpenAI, Claude, Gemini, and open-source models, along with prompt engineering and RAG workflows.
RAG System Development
Connect an LLM to your documents, databases, or internal knowledge sources so it can retrieve relevant information before generating an answer. This is useful for internal knowledge tools, customer-facing applications, and document-heavy workflows.
Conversational AI and Chatbot Development
Create chatbots and assistants capable of understanding user intent and maintaining relevant conversation contexts. This may involve traditional intent-based approaches or LLM-driven assistants, depending on the project.
NLP Pipeline and MLOps
Set up the processes around an NLP model, including data preparation, preprocessing, training, evaluation, deployment, and monitoring. Where required, the setup can also cover drift detection and model retraining.
Foundation Model Expertise
AI Models Our NLP Engineers Work With
Different language models suit different workloads. Our engineers can work with commercial and open-source models and choose an approach based on the task, data, application, and deployment requirements.
OpenAI GPT-4o
Handles text generation, summarization, classification, information extraction, and conversational AI use cases.
Anthropic Claude
Useful for long documents, enterprise knowledge applications, document analysis, and RAG-based workflows.
Google Gemini
Supports NLP applications that combine text with images and other content types.
Meta Llama
Useful when a project needs an open-weight model with flexibility around fine-tuning and deployment.
Mistral
A practical choice for NLP tasks where quick responses, flexible deployment, and affordable operating costs are important.
Grok
This approach is well suited for specific NLP tasks like classification, named entity recognition, and question answering.
Our Tech Stack
The NLP Tech Stack Our Engineers Use
The tools used for an NLP project depend on what needs to be built and where it will run. Our engineers work across the following technologies, from model development and data processing to deployment and monitoring.
Programming Languages
Python
R
SQL
NLP Frameworks and Libraries
spaCy
NLTK
Hugging Face Transformers
Gensim
Stanford NLP
FastText
ML and Deep Learning
PyTorch
TensorFlow
Scikit-learn
Keras
XGBoost
LLM and GenAI Platforms
OpenAI GPT-4o
Anthropic Claude
Google Gemini
AWS Bedrock
Meta Llama
Mistral
Vector Databases
Pinecone
Weaviate
ChromaDB
pgvector
Qdrant
AI Agent and LLM Frameworks
LangChain
LangGraph
LlamaIndex
Semantic Kernel
MLOps and Model Management
MLflow
Kubeflow
DVC
Weights and Biases
BentoML
Cloud and Infrastructure
AWS SageMaker
Google Vertex AI
Azure ML
Docker
Kubernetes
Terraform
Development and Integration
REST APIs
GraphQL
Webhooks
GitHub
CI/CD Pipelines
What We Build
What You Can Build with Our NLP Engineers
01
Text Classification Systems
Organize your emails, documents, reviews, support tickets, and other written content into categories that seamlessly fit into your workflow. Our models can be tailored to understand your specific terminology and classification preferences.
02
Named Entity Recognition Pipelines
Extract information such as names, organizations, locations, dates, medical terms, and industry-specific entities from unstructured text. You can then pass the extracted data to search, report, or other business systems.
03
Document Intelligence and Processing
Reduce manual work around large volumes of documents by using NLP to extract, classify, summarize, and organize information. Applications can support legal, healthcare, finance, operations, and other document-heavy teams.
04
RAG-Powered Knowledge Systems
Connect language models to internal documents, databases, and knowledge bases. Users can ask questions in natural language and retrieve information from the sources connected to the system.
05
Conversational AI and Chatbots
Create assistants for customer service, sales, internal support, and other conversational tasks that feel more human. These systems can understand intent, consider conversation context, and respond naturally over multiple turns.
06
Sentiment and Opinion Analysis Systems
Analyze reviews, surveys, support conversations, customer feedback, and other written responses to understand sentiment and recurring themes across large amounts of text.
07
Question Answering and Search Systems
Build search tools that understand the meaning behind a query instead of relying only on exact keyword matches. Semantic search and question-answering systems can retrieve relevant information from documents and knowledge bases.
08
NLP Integration into Existing Products
Add language capabilities to an existing SaaS product, CRM, ERP, or enterprise application. This can include classification, text analysis, semantic search, document processing, or conversational features without rebuilding the entire platform.
How We Work
Our Engagement Models
Dedicated NLP Engineer
Add a senior NLP engineer to your team full-time. They can own model development, training, testing, and improvements while working directly with your product and engineering teams.
NLP Team Augmentation
Add NLP specialists to your existing team when you need skills that are not available internally. This can help with projects involving language models, RAG, NLP pipelines, or other specialized development work.
Project-Based NLP Development
Start with a defined requirement and have the NLP system developed around an agreed scope and timeline. Development can include testing, deployment, documentation, and handover to your team.
Flexible / Part-Time Engagement
Hire an NLP specialist for a defined number of hours or a specific period. This way, you can easily get help with activities like fine-tuning models, designing pipelines, building RAG architecture, conducting technical reviews, and working on targeted development tasks.
Why Quantal AI
Why Companies Hire NLP Engineers From Quantal
01
Production-Grade NLP Experience
Our engineers work on NLP projects beyond initial prototypes, including data preparation, model training, evaluation, deployment, monitoring, and optimization.
02
End-to-End NLP Development Capability
We can support NLP projects from early data preparation and model selection through fine-tuning, deployment, and MLOps.
03
Hire in 24 Hours
Share your requirements, and Quantal AI can match you with an NLP engineer based on the technical skills and experience your project needs, with onboarding available within 24 hours.
04
US Timezone Availability
Engineers are available across EST and PST, allowing US-based teams to discuss requirements, review work, and resolve project questions during overlapping working hours.
05
Flexible Engagement Models
You can hire NLP developers for a specific task, add them to an existing team, or bring together a larger team for an end-to-end NLP project. We offer dedicated, team augmentation, project-based, part-time, and hourly options.
06
Clean Code and Full Documentation
We deliver projects with organized code and technical documentation to make handover easier. Your internal team can use these materials to maintain the system and continue development.
Get in Touch
Talk to an AI Expert
Who We Serve
Industries We Serve
Healthcare
NLP can support clinical documentation, medical record extraction, patient communication analysis, and other healthcare language workflows. Plan projects involving sensitive information around applicable privacy and security requirements.
Fintech and Banking
Build systems for financial document processing, regulatory text analysis, fraud-related language analysis, and compliance with workflows involving large volumes of financial information.
Legal and Professional Services
Use NLP for contract analysis, document extraction, case summarization, legal research, and text classification across large document collections.
E-commerce and Retail
Apply NLP to product reviews, customer conversations, site search, and support workflows. Common applications include sentiment analysis, support automation, and search relevance.
HR and Recruitment
Build tools for resume parsing, candidate matching, job description analysis, and other recruitment workflows that involve large amounts of text.
SaaS and Technology
Add NLP features to software products through intelligent search, document processing, conversational interfaces, and other language-based product capabilities.
Media and Publishing
Process articles and other content through classification, summarization, topic modelling, sentiment analysis, and other text-processing workflows.
Manufacturing
Work with technical documents, maintenance logs, quality reports, and supply chain records to make text-heavy operational information easier to search, organize, and analyze.
How We Hire
How We Hire Our NLP Engineers
Our screening process evaluates technical knowledge and the ability to apply it. We assess engineers on NLP fundamentals, practical development work, production experience, and communication.
We review knowledge of tokenization, embeddings, sequence modelling, transformer architectures, fine-tuning, and NLP system design.
Engineers complete practical tasks covering text preprocessing, model training, evaluation, and commonly used NLP frameworks, including Hugging Face Transformers, spaCy, LangChain, PyTorch, and TensorFlow.
Candidates work through a practical NLP problem under defined project conditions. Depending on the role, the task may involve building, fine-tuning, or deploying a language model.
We review previous experience with deployed NLP systems, including model monitoring, drift detection, retraining workflows, and cloud environments such as AWS, GCP, and Azure.
We also look at how engineers communicate technical decisions, provide updates, respond to feedback, and work with distributed teams.
Hire an NLP Engineer for Your Project
Have an NLP requirement but need the right technical expertise to get started? Share your project details and preferred hiring model, and Quantal AI can connect you with a vetted NLP engineer within 24 hours.
Proven Results
Success Stories
Leadership Team
Meet Our
Founders
Shailesh Jain
Co-Founder · CMU Alumni
Nirav Shah
Co-Founder · Columbia, Ex UBS
Client Stories
What clients say about us.
“Quantal AI and Team are EXPERTS at building ANY AI functionality you’re seeking! We’ve hired them for 2 projects already — each completed ON TIME and UNDER BUDGET. Highly Recommended!”
Melissa C
myhomecarebiz.com“Working with Quantal AI team has been an absolute pleasure. Their technical aptitude is outstanding — they’re not only highly competent but also creative, thoughtful, and reliable. They built a complex integration for our wine business connecting PhotoRoom, Google Cloud, AWS, and Shopify, and it works beautifully.”
David F
Osteopathic Healing Hands“It was a pleasure working with Quantal AI team. Communication was smooth, deadlines were respected, and the overall collaboration was professional and efficient. I would definitely consider working together again in the future. Recommended!”
Ivana M
Elunic AG
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Read MoreFrequently Asked Questions
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What does an NLP engineer do?An NLP engineer builds software that works with human language. Their work can include preparing text data, training or fine-tuning models, building NLP pipelines, connecting language models to applications, and deploying and monitoring the resulting systems.
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What is the difference between an NLP engineer and a machine learning engineer?An NLP engineer specializes in language-related problems such as text classification, entity recognition, semantic search, language models, and conversational systems. A machine learning engineer can work across a wider range of machine learning problems and data types.
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What skills should a senior NLP engineer have?A senior NLP engineer should understand NLP, machine learning, transformers, language models, data processing, model evaluation, and deployment. Depending on the project, experience with Python, PyTorch, TensorFlow, Hugging Face, spaCy, and related tools can also be important.
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When should I hire an NLP engineer instead of a general AI developer?An NLP engineer makes sense when the main problem involves language or text, such as document processing, information extraction, semantic search, classification, RAG, conversational AI, or language model development.
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How quickly can I hire an NLP engineer from Quantal AI?Quantal AI can match you with an NLP engineer within 24 hours after receiving your project requirements. The start date depends on the engineer's availability and the engagement model you choose.
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What NLP frameworks and tools should an engineer know in 2026?The right tools depend on the project. Common choices include Hugging Face Transformers, spaCy, PyTorch, TensorFlow, LangChain, LangGraph, vector databases, cloud AI platforms, and MLOps tools. Practical experience with the stack your project uses are more important than knowing every available tool.
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Can an NLP engineer fine-tune a language model on my proprietary business data?Yes, fine-tuning can be used for certain models and applications. Whether it makes sense depends on the task, data quality, amount of training data, privacy requirements, and the result you want. For some projects, RAG or prompt engineering may be a better fit.
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How much does it cost to hire an NLP engineer?Pricing depends on the engineer's experience, project requirements, engagement model, technical scope, and other factors such as timezone or availability. Dedicated, hourly, part-time, and project-based engagements can have different costs.
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What is the difference between NLP and LLM development?NLP is the broader field of working with human language and includes tasks such as classification, entity recognition, sentiment analysis, information extraction, and language understanding. LLM development focuses on large language models and applications built around them, including RAG, fine-tuning, agents, and generative systems.
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How do I know if an NLP engineer has real production experience?Look beyond prototypes and demos. Experience with deployed systems, model evaluation, monitoring, data pipelines, integrations, cloud deployment, retraining, and post-launch application maintenance can better indicate production experience.
Hire the Right NLP Engineer for Your Project
Share your project requirements, and we’ll connect you with an NLP engineer whose skills and experience match your needs.
Or email us directly at contact@quantaltech.ai or call +1 315 809 3225.