4+
Years Writing
50+
AI/ML Articles
20+
Tools Covered
24h
Response Time
01Why Hire Me
Good AI/ML writing is rare.
Here's what makes mine different.
01
Technical accuracy first
I understand transformer architectures, attention mechanisms, training pipelines, and data engineering stacks — not just the terminology. Your ML engineers and data scientists will not cringe reading what I write.
02
Keeps up with the AI pace
The AI landscape shifts weekly. I follow model releases, research papers, and framework updates actively. Articles on GPT-4, Claude 3, Llama, Mistral, and emerging tools are grounded in current reality.
03
Bridges devs and decision-makers
I can write the same topic at two depths: a deep technical tutorial for ML engineers, or an accessible explainer for CTOs and product teams. Most AI content fails one audience. Mine doesn't.
04
SEO built for AI queries
AI and ML search queries have high specificity. I research intent at the keyword level — "fine-tuning vs RAG" ranks differently than "LLM fine-tuning guide". Every piece is built around what your audience actually searches.
02Topic Coverage
AI/ML topics I cover.
Generative AI & LLMs
- GPT-4 / GPT-4o deep dives
- Claude, Gemini, Llama architecture
- Prompt engineering guides
- RAG (Retrieval-Augmented Generation)
- Fine-tuning and RLHF
- LLM evaluation and benchmarks
ML Fundamentals & Deep Learning
- Supervised & unsupervised learning
- Neural network architectures
- CNNs, RNNs, Transformers
- Diffusion models
- Reinforcement learning
- Model training & optimisation
Data Engineering
- Apache Spark & Databricks
- Delta Lake & Lakehouse architecture
- dbt data transformation
- Azure Data Factory & Synapse
- Data pipeline design
- ETL vs ELT patterns
MLOps & Production AI
- Model deployment strategies
- Model monitoring & drift detection
- ML pipelines (Kubeflow, MLflow)
- Feature stores
- A/B testing for models
- Responsible AI & fairness
NLP & Computer Vision
- Text classification & embeddings
- Semantic search & vector databases
- Named entity recognition
- Image classification & object detection
- Multimodal models
- CLIP, Stable Diffusion
AI Tools & Platforms
- Hugging Face Transformers
- LangChain & LlamaIndex
- OpenAI & Azure OpenAI API
- AWS SageMaker
- Vertex AI (Google)
- Weights & Biases, Comet ML
03Tools & Platforms
Tools and frameworks I write about.
PyTorchTensorFlowHugging FaceLangChainLlamaIndexOpenAI APIAzure MLDatabricksApache SparkdbtMLflowScikit-learnKerasCUDAVertex AIAWS SageMakerWeights & BiasesPineconeWeaviateFAISS
04Content Types
What I write for AI/ML companies.
Model Architecture Deep Dives
Long-form technical articles explaining how models actually work — attention, tokenisation, positional encoding, training objectives. Written for engineers who want to understand, not just use.
Practical ML Tutorials
Step-by-step code tutorials for building with ML frameworks. Every example is tested, every snippet is clean. PyTorch, Hugging Face, LangChain — with real use cases, not toy examples.
Data Engineering Guides
In-depth guides on modern data stacks — Databricks, Spark, dbt, Delta Lake, and cloud-native pipelines. Bridging the gap between data engineering and ML workflows.
Research Paper Breakdowns
Making cutting-edge AI research accessible to practitioners. Translating papers like "Attention Is All You Need" or the Llama 3 technical report into actionable engineering insights.
Comparison & Evaluation Articles
"Fine-tuning vs RAG", "PyTorch vs TensorFlow in 2025", "Vector database comparison" — balanced, technically grounded comparisons that developers bookmark and reference.
AI Product & Platform Content
Documentation, case studies, and product blog posts for AI SaaS platforms. Content that explains your ML product's value to both technical users and business buyers.
05How It Works
How to hire me for AI/ML writing.
01
Share your AI/ML brief
Tell me the topic, target audience (ML engineers, data scientists, or business decision-makers), technical depth, and any specific tools or frameworks to cover. The more context, the better the output.
02
Get a technical outline + quote
I send a detailed outline within 24 hours — section breakdown, key technical points to cover, target keywords, and a fixed price. You approve before I write a single word.
03
Receive accurate, ranked content
First draft delivered within 5–7 business days. Technically reviewed, SEO-optimised, and formatted for your CMS or docs platform. One revision round included.
06Engagement Models
Ways to work together.
Per Article
From $20
One-off AI/ML articles, tutorials, or model explainers. Ideal for a single topic or testing the fit before a retainer.
- Topic & keyword research
- Technical outline review
- Full article (2,000–5,000 words)
- Code examples if needed
- One revision round
Most Popular
Monthly Retainer
4–8 articles/mo
Consistent AI/ML content for platforms, blogs, and developer hubs. Priority queue, faster turnaround, and a writer who learns your product.
- AI/ML content strategy
- Priority scheduling
- Cross-article consistency
- Monthly topic review
- Flexible format mix
Project-Based
Custom quote
Documentation for an ML platform, a pillar cluster on generative AI, or a full content audit. Scoped per project with agreed milestones.
- Scope & timeline upfront
- ML platform docs
- Content clusters & pillars
- Structured milestones
- Revisions included
All pricing in USD · Custom quotes for large projects · Rush delivery available
07Client Feedback
What clients say.
“Krunal delivered a 12-part blockchain deep-dive series that became our highest-traffic content. He understands the tech at a level most writers simply don't — smart contracts, DeFi mechanics, Layer 2 trade-offs. Our developer audience loved it.”
A
Arjun Mehta
CTO, ChainVerse Labs
“We needed someone who could write about ML pipelines and data engineering without dumbing it down. Krunal nailed it. His articles on Databricks and Azure ML drove 3x more organic traffic than our previous content. He's now our go-to writer for anything technical.”
S
Sarah Chen
Head of Content, DataStack AI
“Krunal wrote our entire developer documentation and a series of technical tutorials. The quality was exceptional — clear code examples, proper structure, and he actually tested everything he wrote. Turnaround was fast and communication was seamless.”
R
Rahul Sharma
Founder, DevToolkit
“Finding a writer who can explain Web3 concepts to both developers and business stakeholders is rare. Krunal did exactly that for our case studies and services pages. The content directly contributed to closing two enterprise deals.”
E
Emily Rodriguez
Marketing Director, NexGen Protocol
08Who You're Hiring
Krunal Kanojiya
Technical Content Writer · Ahmedabad, India
Former full-stack developer turned technical writer. I started writing about AI and ML because I was already building with these tools as a developer. That background means I understand the models, the data pipelines, and the infrastructure — not just the surface-level concepts that most AI writers cover.
LLMs & Generative AIData EngineeringMLOpsPyTorchHugging FaceLangChain
09FAQ
Frequently asked questions.
Who is Krunal Kanojiya as an AI and ML technical writer?
Krunal Kanojiya is a technical content writer from India with 4+ years of experience specialising in AI, machine learning, data engineering, and blockchain. He is a former full-stack developer who writes accurate, in-depth articles on topics like LLMs, generative AI, ML pipelines, and data engineering tools for developer and technical audiences.
What AI and machine learning topics can Krunal write about?
Krunal writes about generative AI and LLMs (GPT-4, Claude, Llama, Mistral, RAG, fine-tuning), ML fundamentals (neural networks, transformers, CNNs, diffusion models), data engineering (Databricks, Apache Spark, dbt, Delta Lake, Azure ML), MLOps (model deployment, monitoring, MLflow, Kubeflow), NLP (embeddings, semantic search, vector databases), computer vision, and AI tools like Hugging Face, LangChain, and LlamaIndex.
How much does it cost to hire an AI/ML technical writer?
AI and ML articles start from $20 per piece. Pricing depends on the technical depth, length (typically 2,000–5,000 words), and research required. Monthly retainer packages for 4–8 articles per month are available for ongoing content needs. Project-based work (documentation, content clusters) is quoted per scope. Email imkrunalkanojiya@outlook.com for a custom quote.
Can Krunal write about the latest AI models and research papers?
Yes. Krunal follows AI research actively and writes about the latest model releases, research papers, and framework updates. He has written breakdowns of major papers and covered new releases from OpenAI, Anthropic, Meta AI, and Google DeepMind. Content is grounded in current, accurate information.
Can Krunal write AI/ML content for both technical and non-technical audiences?
Yes. He adjusts depth and tone based on the target audience. For ML engineers and data scientists, content includes architecture details, code examples, and mathematical intuition. For product managers, CTOs, and business audiences, the same topics are covered with analogies, practical implications, and business context — without sacrificing accuracy.
Does Krunal write practical ML tutorials with working code?
Yes. Tutorials include tested, working code in Python using frameworks like PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and Scikit-learn. Code examples are clean, commented where helpful, and validated before publishing.
What data engineering platforms and tools does Krunal cover?
Krunal writes about Databricks, Apache Spark, dbt (data build tool), Delta Lake, Azure Data Factory, Azure Synapse Analytics, AWS Glue, Google BigQuery, Apache Kafka, Airflow, and modern data lakehouse architectures. He covers both the tool mechanics and the broader data engineering patterns.
How long does it take to get an AI/ML article delivered?
Most AI/ML articles (2,000–3,000 words) are delivered within 5–7 business days from the approved outline. Longer deep dives (4,000–5,000 words), research paper breakdowns, and documentation projects are scoped individually. Rush delivery is available for an additional fee.
What is the process for hiring Krunal for AI/ML writing?
The process: (1) Email with your topic, target audience, and technical depth needed. (2) Receive a detailed technical outline and fixed-price quote within 24 hours. (3) Approve the outline. (4) First draft delivered in 5–7 business days. (5) One revision round included. (6) Final delivery in Markdown, Google Docs, or CMS-ready format.
Is Krunal available for ongoing AI/ML content work?
Yes. He works with AI companies and data platforms on monthly retainers — typically 4–8 articles per month. Retainer clients get priority scheduling, faster turnaround, and consistent coverage of their AI domain. Email imkrunalkanojiya@outlook.com to discuss availability.
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