You will get Custom ML Model Fine-Tuning & Machine Learning Pipeline
Project details
You will get a custom machine learning model — trained, fine-tuned, and deployed — built specifically for your data and business problem. I work across the full ML spectrum: from traditional scikit-learn pipelines for tabular data to fine-tuning large language models (LLMs) using LoRA/QLoRA with Hugging Face. Whether you need a classification model, a regression predictor, an NLP pipeline, or a fine-tuned GPT/Llama/Mistral for your domain-specific use case, I deliver production-quality code with thorough evaluation metrics, clear documentation, and a deployment-ready API. I've built ML systems for healthcare prediction, e-commerce recommendation, fraud detection, and text classification.
Machine Learning Tools
Azure Machine Learning, BERTWhat's included
| Service Tiers |
Starter
$400
|
Standard
$900
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Nilesh
AIML Developer & Data Engineer | LLMs, RAG, ML Pipelines | Snowflake,
Pune, India - 8:57 pm local time
If you need AI that works in production — not just prototypes — I'm your engineer.
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🤖 AI / ML DEVELOPMENT
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• LLM Integration & Fine-tuning: OpenAI, Gemini, AWS Bedrock, Anthropic Claude
• RAG Systems & AI Agents: LangChain, LlamaIndex, vector DBs (Pinecone, Weaviate)
• ML Models: XGBoost, LightGBM, Scikit-learn — churn, forecasting, NLP classification
• ML Pipelines: feature engineering, hyperparameter tuning, model deployment
• Production AI on AWS SageMaker & Azure ML
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🔷 DATA ENGINEERING & PIPELINES
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• End-to-end ETL/ELT: AWS Glue, Azure Data Factory, Apache Airflow, dbt
• Cloud Data Warehouses: Snowflake, BigQuery, Redshift, Azure Synapse
• Data Lakehouses: Databricks Delta Lake, Apache Spark, PySpark
• Real-time & batch pipelines processing millions of records daily
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🏆 SELECTED WINS
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• Financial AI Analyst — RAG system ingesting financial filings; delivered cited business insights via LLM
• Automotive Churn Engine — hybrid ML model on 730-day behavioral data; enabled proactive retention
• Enterprise Data Platform — Snowflake + Azure Data Factory; ~30% latency reduction, ~40% efficiency gain
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🛠 TECH STACK
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AI/ML: Python • LangChain • OpenAI • LlamaIndex • XGBoost • Scikit-learn • NLP • LLMs
Cloud: AWS (Certified SA) • Azure (Certified) • GCP
Data: Snowflake • Databricks • Spark • dbt • Airflow • BigQuery
BI: Power BI • Tableau • Looker
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Ready to build AI that drives real results? Let's connect.
Steps for completing your project
After purchasing the project, send requirements so Nilesh can start the project.
Delivery time starts when Nilesh receives requirements from you.
Nilesh works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis & Preprocessing
Explore your dataset, handle missing values, engineer features, split train/test, and establish baseline metrics before model selection.
Model Training, Tuning & Delivery
Train and fine-tune the model (LoRA/QLoRA for LLMs), optimize hyperparameters, evaluate on test set, wrap in API (FastAPI), and deliver with documentation.