You will get AI/ML algorithm trained on image and video data (Any Industry/Domain)
Top Rated
Project details
You will get a complete robust AI/ML model to process your images and perform object detection, classification, comparison and meet your business goals.
Machine Learning Tools
Azure Machine Learning, Caffe, deeplearn.js, Deeplearning4j, Google AutoML, Keras, Kubeflow, MLflow, NumPy, NVIDIA AI Platform, OpenCV, Python, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$2,100
|
Standard
$3,100
|
Advanced
$5,100
|
|---|---|---|---|
| Delivery Time | 15 days | 30 days | 45 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code |
84 reviews
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ES
Eduardo S.
Aug 13, 2026
Data Engineer for MVP Prototype Development
It was a great experience working with Gaurav. He was professional, reliable, responsive, and demonstrated strong technical knowledge throughout our collaboration. Communication was clear, and he was always willing to review feedback, answer questions, and work through issues when needed.
I especially appreciated his commitment to the work and his willingness to provide explanations and support throughout the process.
Overall, I had a very positive experience and would be happy to work with Gaurav again in the future. Highly recommended.
I especially appreciated his commitment to the work and his willingness to provide explanations and support throughout the process.
Overall, I had a very positive experience and would be happy to work with Gaurav again in the future. Highly recommended.
MA
Mirabbos A.
Jul 7, 2026
Data Warehouse and ETL Development for Insurance Company
DZ
Devs Z.
Jan 5, 2026
Azure AI Engineer for Zezamii Vision Platform
IS
Itamar S.
Oct 30, 2025
Data engineer expert
showed really good knowledge working with data. tested several new tools in the data realm that we are using with customers and did it efficiently. Easy to communicate with. Thanks!
JS
Josh S.
Oct 14, 2025
Data Engineer, Data Pipeline ETL expert
Excellent experience, very knowledgeable with his work, would work with again in the near future
About Gaurav
AI Engineer | LLM, RAG, MCP & AI Agents in Production | SOC 2 , HIPAA
98%
Job Success
Indore, MP, India - 8:32 pm local time
I assist startups and corporations to take their AI from the concept stage to actual production systems - whether it is AI Agents, intelligent processes, RAG use cases, voice AI, document processing, data flows, or AI-powered automation for business purposes.
Enterprise and Compliance Experience:
I have 20+ years of experience in software/data engineering and have worked on enterprise-level projects at DXC, Lincoln National, Jackson National, and Cisco.
My experience includes the development of systems for environments where SOC 2, HIPAA, GDPR, and CMMI Level 5 requirements applied.
Such experience is important in case an AI system requires not only working prototype but also works in a complex environment such as finance, insurance, healthcare, etc.
What I build
AI Agents & Agentic Systems
• Multi-agent systems using LangGraph, CrewAI, AutoGen and MCP
• Tool agents integrated with various APIs, databases and business applications
• Agent orchestration, agent memory, agent routing and human-in-the-loop workflows
• Research, data extraction, reporting and operational workflow automations
LLM & RAG Applications
• Enterprise production RAG solutions
• Semantic/hybrid search using Pinecone, Weaviate, Qdrant, Milvus and pgvector
• LangChain, LlamaIndex and GraphRAG applications
• Document ingestion, chunking, embedding, retrieval and evaluation
• OpenAI, Anthropic Claude, Gemini and open-source LLM integrations
• Structured LLM prompts and function calls
AI Automation
• n8n-based AI-powered workflows and business processes automation
• Integration of AI with CRMs, databases, various APIs and internal applications
• Zapier, Make and custom Python-based automations
• Automation pipelines from web/data extraction to processing, analysis, reporting
• Sales, support, operations, research and document processing workflows powered by AI
Voice AI
• AI voice agents and conversational workflow automations
• Vapi, Retell, Twilio, Deepgram, ElevenLabs and other voice technologies
• Automated intake, qualification, scheduling, support and follow-up workflows
The Production Engineering Behind the AI : One of my strongest advantages is that I have a solid software and data engineering background, which means I don't see AI as an isolated API integration problem.
I develop the infrastructure needed for AI to work reliably:
• Python, FastAPI, Node.js and REST/GraphQL APIs
• PostgreSQL, MongoDB, Redis and other databases
• AWS, Azure and Google Cloud
• Docker, Kubernetes and Terraform
• CI/CD and production deployment
• Logging, monitoring and error handling
• Data validation and pipeline reliability
• MLOps and model monitoring
Data Engineering for AI : A lot of AI projects are doomed to failure due to the underlying data being of low quality.
I create the data layer AI projects depend on:
• ETL/ELT pipelines
• Airflow and dbt
• Spark and Python data processing
• Snowflake, BigQuery and Databricks
• Kafka/Kinesis for streaming workloads
• Processing of structured and unstructured data
• Data extraction, transformation and enrichment pipelines
If you are struggling with your AI project delivering bad results due to the underlying data being messy, partial or hard to get, this is where I can help the most.
My AI Stack:
- AI/LLM: OpenAI, Claude, Gemini, Hugging Face, Amazon Bedrock
- AI Agents: LangGraph, CrewAI, AutoGen, MCP, Semantic Kernel
- RAG: LangChain, LlamaIndex, GraphRAG, Pinecone, Weaviate, Qdrant, Milvus, pgvector, FAISS
- Automation: n8n, Make, Zapier, Flowise, LangFlow
- Voice AI: Vapi, Retell, Twilio, Deepgram, ElevenLabs, Whisper
- LLM Engineering: Prompt Engineering, Function Calling, Structured Outputs, Fine-tuning, LoRA/QLoRA, PEFT, vLLM
- Backend: Python, FastAPI, Django, Flask, Node.js, Express.js
- Cloud/Infrastructure: AWS, Azure, GCP, Docker, Kubernetes, Terraform
- Data: Snowflake, BigQuery, Databricks, Airflow, dbt
My approach is to build for production and transfer, and not just for a good demo – with clear architecture, documentation, monitoring, and maintainable code which can be further worked on by your internal team.
In case you’re looking for an AI Engineer who can help you build AI Agents, LLMs/RAGs, n8n automations, voice AI or the underlying infrastructure of all of the above, do get in touch with me.
--
Best Regards,
Gaurav Goyal
Steps for completing your project
After purchasing the project, send requirements so Gaurav can start the project.
Delivery time starts when Gaurav receives requirements from you.
Gaurav works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement gathering and data exploration
I will start by understanding your requirements closely and exploring the data that you have to check the quality of data and what can be achieved with it to align with your use case.