Language understood, not just parsed
Hire a freelance NLP developer who ships to production
Most NLP projects stall between proof-of-concept and production. I bridge that gap by designing, training, and deploying NLP systems that process real language data at scale without breaking in the wild.
Available now, projects start within 48 to 72 hours, NDA before any data moves
Plain answer
What does a freelance NLP engineer actually do?
A freelance NLP engineer designs and builds systems that enable software to read, understand, and act on human language, text or speech. Core deliverables include custom text classifiers, sentiment analysis pipelines, information extraction systems, chatbots, LLM fine-tuning, and search intelligence. Unlike a generalist ML developer, an NLP engineer specialises in language-centric architectures: transformer models (BERT, RoBERTa, LLaMA), tokenisation pipelines, embedding generation, and entity extraction.
A freelance NLP developer works independently, meaning lower overhead than an agency, direct access to the engineer building your system, and faster iteration cycles. Engagements typically run 2 to 16 weeks, from a scoped proof-of-concept to full production deployment with CI/CD and monitoring.
Why hire freelance
Freelance NLP developer vs NLP agency vs in-house hire
A straight comparison so you can weigh cost, speed, and specialisation before deciding.
| Factor | Freelance NLP developer | NLP agency | In-house hire |
|---|---|---|---|
| Cost | $, project-based | $$$, retainer plus markup | $$$$, salary plus benefits |
| Speed to start | 48 to 72 hours | 2 to 4 weeks | 3 to 6 months |
| Direct access to builder | Always | Account manager layer | Yes |
| Domain specialisation | Deep NLP focus | Generalist teams | Varies |
| Flexible scope | Scale up or down | Locked contracts | Fixed headcount |
When freelance NLP is the right call
You have a defined NLP problem, text classification, NER, chatbot, document extraction, need it solved within a quarter, and want a senior engineer, not a junior allocated by a staffing firm.
Run the numbers first
What manual reading and tagging is costing you
Reviews, tickets, contracts, applications, whatever your team is reading and tagging by hand. Move the sliders to match your volume.
Estimate only, based on delivered projects. The remaining share still routes to a human, by design, for anything ambiguous or high-stakes. Your actual automation coverage is confirmed against real data in the discovery phase, not assumed.
What I build
How can NLP automate your language workflows?
Systems that help machines understand and work with human language: automating communication, analysing text data, and supporting smarter decisions. Violet tags are grounded conversational systems. Amber is the ongoing operations layer.
Custom NLP model development
Transformer-based NLP models designed from scratch or fine-tuned from BERT, RoBERTa, LLaMA 3, or Mistral on your domain-specific language data. Output is a production-ready model plus inference API.
- Text classification and multi-label tagging
- Domain-adaptive fine-tuning
- Low-latency ONNX / TorchScript export
Sentiment and emotion analysis
Turn customer reviews, survey responses, and social mentions into structured sentiment signals, going beyond positive or negative to surface why customers feel what they feel.
- Aspect-based sentiment analysis (ABSA)
- Emotion detection: anger, trust, surprise
- Dashboard-ready output via API or database
Named entity recognition
Extract people, organisations, products, dates, and custom entities from unstructured documents at scale. Critical for contract analysis, compliance, CRM enrichment, and knowledge graph construction.
- Custom entity types beyond generic NER
- Legal, medical, and financial domain models
- Production pipeline with 95%+ precision benchmarks
Chatbots and conversational AI
Chatbots grounded in your actual business data, not generic LLM wrappers. RAG-based architectures ensure answers are accurate, traceable, and updated without retraining.
- RAG chatbots: LlamaIndex / LangChain
- Intent classification plus slot filling
- Multilingual support: English, Hindi, Spanish, and more
Document intelligence and information extraction
Automate extraction from invoices, contracts, reports, and forms. I combine NLP with layout-aware models such as LayoutLM and Donut to handle both text-heavy and document-structured inputs.
- Key-value extraction from PDFs
- Contract clause detection
- Table and form parsing
Text mining and topic modelling
Discover hidden themes in customer feedback, support tickets, or research corpora using BERTopic and LDA-based pipelines with human-readable labels.
- Automated theme discovery at scale
- Trend tracking across time periods
- Exportable topic-document mappings
LLM fine-tuning and prompt engineering
Fine-tune open-source LLMs such as LLaMA 3, Mistral, and Phi-3 on proprietary datasets using QLoRA for cost-efficient adaptation, plus production-grade prompt chains that reduce hallucination.
- QLoRA / LoRA fine-tuning on GPU
- RLHF-style preference alignment
- Structured output enforcement: JSON schema
NLP API integration and deployment
NLP models deployed as production APIs on AWS, GCP, or Azure. Containerised with Docker, monitored with MLflow and Prometheus, and versioned for continuous improvement.
- FastAPI + Docker deployment
- Model drift monitoring
- CI/CD pipeline for retraining
Not sure which one you need?
A 30-minute discovery call clarifies scope before anything is billed.
Talk it throughThe idea behind the demo above
Domain fine-tuned vs generic pretrained
A generic pretrained language model has seen the internet, not your business. It knows what a date and a company name generally look like, but it has never seen "credentialing review" as a meaningful event, or that your support team treats "payment screen crash" as a severity-one issue rather than a passing complaint. Domain fine-tuning teaches the model your vocabulary, your entity types, and your definition of what matters, which is why the same architecture performs so differently on the same sentence.
When the task is broad and the vocabulary is common
- General-purpose sentiment or topic tagging with no custom taxonomy
- Prototyping, before you know which entities or aspects actually matter
- Low volume, where the cost of fine-tuning outweighs the accuracy gain
When accuracy on your specific language pays for itself
- A custom entity or aspect taxonomy specific to your business
- Legal, medical, or financial vocabulary generic models get wrong
- Any workflow where a missed or mislabelled item has a real cost
The demo above uses the same three tasks in both modes so the difference is visible, not asserted. That is the same standard I hold any model to before it ships.
Where it runs
Industries I have built NLP solutions for
Domain-adapted dataset experience and existing model benchmarks across these verticals mean faster time-to-accuracy for your project.
Ecommerce
Product review classification, search query understanding, catalog description generation.
Healthcare
Clinical note extraction, ICD code suggestion, medical record summarisation.
SaaS and B2B
Support ticket routing, churn signal detection from NPS text, and contract clause analysis.
Fintech
Financial document parsing, earnings call sentiment, regulatory filing extraction.
HR and recruiting
Resume parsing and matching, job description optimisation, candidate intent scoring.
Pricing
What it costs to hire a freelance NLP developer
Rates vary by scope and complexity. Contact me for a scoped estimate based on your specific requirements.
Proof of concept
A text classifier, basic NER model, or scoped chatbot prototype. The cheapest way to validate the approach.
Full production
Fine-tuning, API integration, monitoring, and documentation, in one scoped engagement.
Dedicated NLP engineer
Set weekly hours for an ongoing NLP roadmap. Same standing offer as my other AI service lines.
Hourly consulting
Senior NLP engineering advisory. Useful before committing to a build.
How it gets built
How I build intelligent NLP solutions
I start by understanding the language data and project goals, then prepare text data, train and fine-tune models, and deploy solutions that work reliably in real-world applications.
PHASE 01days 1 to 2Discovery
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PHASE 02the decisive oneData assessment and preprocessing
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PHASE 03after data is readyModel architecture and baseline
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PHASE 04iterated to targetTraining, evaluation, and iteration
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PHASE 05handoffDeployment and integration
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PHASE 0630 days plusHandoff and ongoing support
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Tooling
Technologies powering NLP solutions
Modern NLP technologies used to build systems that understand and work with human language, supporting chatbots, sentiment analysis, document processing, intelligent search, and real-time text insights.
Features
Why work with Shreyans Padmani
Building NLP solutions that help systems understand text, automate communication, and improve user interactions.
Custom NLP models
Every business handles text differently, so I build NLP models tailored to your specific needs, whether that is text classification, sentiment analysis, or document processing.
Text & language automation
Systems that can read, analyze, and generate text automatically, helping reduce manual work and improve response times.
Seamless integration
NLP solutions are designed to connect smoothly with your existing applications, chat systems, or internal tools without disrupting your workflow.
Delivered work
Deep expertise in advanced AI technologies
Three delivered projects with the numbers that actually shipped.
AI resume screening: custom NER plus ranking model on 10,000+ resumes
A hiring platform needed automated candidate shortlisting. I trained a custom NER model for skills, education, and experience extraction, and a ranking model for job-fit scoring. Deployed on AWS, processing 10,000+ PDFs per month.
AI video intelligence: Whisper plus extractive NLP summarisation pipeline
A training and enablement platform needed automatic meeting and video summaries. I built an end-to-end pipeline: Whisper for transcription, a custom extractive NLP model trained on meeting corpora for summarisation.
AI customer feedback classifier: 14-class BERT fine-tune on 8,000 labels
Manual survey analysis took 3 days per reporting cycle. I fine-tuned a BERT model on 8,000 labelled open-text feedback records across 14 business categories.
About
Freelance NLP engineer
I am an independent AI developer specialising in NLP and language intelligence systems. Over the past 5+ years I have built production NLP solutions for companies in SaaS, healthcare, fintech, and ecommerce, from initial scoping through to deployment and monitoring.
My NLP stack is grounded in Hugging Face Transformers, spaCy, and modern LLM fine-tuning techniques. I work directly with clients, no account managers, no outsourced build teams. When you hire me, I am the person writing your code.
FAQ
