CEO @ HiCounselor | 2X O-1 Visa Holder | Ex-Deloitte, PwC | Building a Community to Help Job Seekers with Referrals

I came to the US in 2014 for a master's degree at Duke University.

Post-graduation, I was fortunate to start my career as a Tech Consultant at PwC and then with Deloitte in San Francisco. 

However, I always wanted to utilize my tech skills to start my own business. 

In 2020, I decided to leave my job and launched HiCounselor in San Francisco.

The decision wasn't easy as I had many open questions, concerns about immigration, and fear of failure. 

Fortunately, the decision turned out to be the right one. 
✅ Generated millions in revenue
✅ Raised VC funds from top investors in Silicon Valley
✅ Employ more than 40 people in the US, Canada, and India.

👉 These four years have taught me a lot about starting a company as an immigrant in the US, and I want to share this journey with every technical professional who wants to make side income or become a full-time entrepreneur.

✉️ Join my newsletter (https://lnkd.in/gPBziwZ3), where I share weekly actionable tips. 


👉  If you are not ready to leave your full-time job at this time, that's okay.
You can join hicounselor.com/mentor and make a side income by sharing your technical expertise. 

If you need any help, feel free to message me or comment on my posts. I will respond as soon as possible.

Aditya Sharma

𝗪𝗵𝘆 𝗱𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝘀𝘁𝗶𝗹𝗹 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲? 🤔
Most hallucinations are actually RAG stack failures caused by broken context.

When retrieval is weak, the model does exactly what it was trained to do. It completes the pattern using the wrong information.

This is why understanding the full RAG stack matters.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗥𝗔𝗚 𝗦𝘁𝗮𝗰𝗸 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 👇

1. Ingest & Data Processing
↳ Collect, clean, and structure raw data
↳ Poor ingestion leads to poor retrieval
Tools: Kubeflow, Apache Airflow, Apache NiFi, LangChain Loaders, Haystack Pipelines, OpenSearch

2. Embedding Models
↳ Convert text into semantic meaning
↳ Determines how well the system understands similarity
Tools: HuggingFace Transformers, Sentence Transformers, JinaAI, Cognita, Nomic, LLMWare

3. Vector Databases
↳ Store embeddings efficiently for fast lookup
↳ Enables scalable semantic search
Tools: Milvus, Weaviate, PgVector, Chroma, Qdrant

4. Retrieval & Ranking
↳ Finds the most relevant context for each query
↳ Weak retrieval = confident hallucinations
Tools: FAISS, Weaviate, JinaAI Rerankers, Haystack Retrievers

5. LLM Frameworks & Orchestration
↳ Connect pipelines, tools, and workflows
↳ Ensure reliability end to end
Tools: LangChain, LlamaIndex, Haystack, CrewAI, HuggingFace

6. LLMs (The Responder)
↳ Generate answers grounded in retrieved context
↳ Model size matters less than context quality
Models: LLaMA, Mistral, Phi-2, DeepSeek, Qwen, Gemma

7. Frontend Layer
↳ Where users interact with intelligence
↳ Trust is built at the interface
Tools: NextJS, Streamlit, VueJS, SvelteKit

🔥 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
Most hallucinations are not caused by bad models.
They’re caused by bad context.

📌 𝗚𝗲𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗟𝗶𝘀𝘁 (𝗳𝗿𝗲𝗲): https://lnkd.in/gHRjrXQG

👉 Follow me Aditya Sharma for more and 🔄 Repost this to help others use AI

#AI #RAG #OpenSource

Helping Top 1% AI Talent Land Role at Elite AI Companies | Turning Ideas into Real Businesses Using AI | 160k+ Followers | Ex-Deloitte & PwC

Everyone is talking about Agentic AI…
but almost no one tells you when to use which tool.

So I made this simple breakdown 👇
Top 10 Agentic AI tools and where they actually shine.

Most people jump straight into building agents
→ then struggle with chaos, loops, or over-engineering.


𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲:
↳ Some tools are great for automation
↳ Some for multi-agent collaboration
↳ Some for RAG + memory
↳ Some for enterprise workflows
↳ Some just for quick demos


𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 👇

> 𝗻𝟴𝗻 → Use this when you want fast workflow automation, lots of app integrations, or self-hosted control with minimal code.

> 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 → Use this when multiple AI agents need to talk, debate, and collaborate on complex problems.

> 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 → Use this when you’re building real AI products with LLMs, tools, memory, or advanced RAG pipelines.

> 𝗠𝗮𝗸𝗲.𝗰𝗼𝗺 → Use this when non-technical teams need visual, no-code automation across SaaS tools.

> 𝗖𝗿𝗲𝘄𝗔𝗜 → Use this when agents need clear roles (researcher, planner, executor) and structured teamwork.

> 𝗙𝗹𝗼𝘄𝗶𝘀𝗲 → Use this when you want drag-and-drop AI agents or quick RAG prototypes.

> 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 → Use this when agents must plan, remember, loop, and handle complex state-based logic.

> 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘁𝗮𝗰𝗸 → Use this when you need quick demos with built-in tools, memory, and function calling.

> 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 → Use this when your AI must deeply understand and reason over private or structured data.

> 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 → Use this when you’re building enterprise-grade, scalable, memory-aware AI systems.

The biggest mistake people make?
Using enterprise tools for prototypes
or prototype tools for production

📌 𝗚𝗲𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗟𝗶𝘀𝘁 (𝗳𝗿𝗲𝗲): https://lnkd.in/gHRjrXQG

👉 Follow me Aditya Sharma for more and 🔄 Repost this to help others.

#AgenticAI #AIAgents #AIEngineering

AI Agents are quickly becoming the foundation of modern automation, products, and AI-powered businesses.

In this session of the AI for Everyone series, this beginner-friendly live workshop will help you understand how AI agents actually work, how they learn, and how you can start using them confidently even if you are new to AI.

This is a conceptual + practical fundamentals session, designed to remove confusion and build strong foundations.

What you’ll learn in this 30-minutes live session:

⚡ AI Agent Fundamentals
Understand what AI agents are, how they differ from AI tools, and where they are used.

🤖 How AI Agents Learn and Make Decisions
Learn the core concepts behind memory, tools, reasoning, and automation.

🛠 Real Examples and Workflows
See how AI agents operate in real scenarios across business and personal workflows.

🌟 Getting Started the Right Way
Learn how beginners, professionals, and founders can start building and using AI agents correctly.

Whether you are a student, beginner, recruiter, founder, tech professional, or simply curious about AI, this session will give you clarity and confidence to move forward.

✨ Free to attend. Register now to save your spot.

Session 16: Fundamentals of Learning AI Agents

KPMG

•Writing complex queries in SQL Server & 
• Creating reports in  Power Bi Visualisation tool
• Developing Applications by using Power apps with multiple SharePoint databases

Senior Analyst IN Global Delivery Reporting & Analytics

PISoftek

• Engaged on end to end development, loaded data from OLTP/ODS to STAGE , from STAGE to Data Warehouse & from Data Warehouse to Data Mart as per the client requirements, extracted the historical data then that data connect to tableau & power bi for visualization(mainly use tableau)
• With the help of Informatica PowerCenter ETL Tool extracted data from  OLTP & ODS and transformed by creating different transformations like SCD 1 & SCD 2 , CDC/incremental load.

Senior Business Intelligence Analyst

• Engaged into internal projects & also into client projects ,
• Developed various reports in Power Bi (Dax , Power Query)

Associate Consultant

Tata Consultancy Services

• Developed & created mappings transformed data and loaded data.From OLTP database to loaded data to datalakes/data warehouse data came to Denodo and from Denodo with help of putty  connected to tableau, Data Visualization with Tableau with data sources SQL, Denodo ,& ODBC.
• Data Analytics & Visualization , MS SQL Server, amazon redshift with  Tableau,
Later on worked in  Informatica Power Center to load data from one data source to another, creating various transformations,
from there we connected data into tableau/power bi as per requirement of clients

Senior Process Associate IN Data Technology 

R1 RCM

• Worked on report & dashboard developments with the help of  Tableau and Power Bi Visualisation tools for US healthcare
• Worked as a Claim Analyst from provider side US healthcare domain for 1 year

Senior Analyst

Genpact

• Worked as a Support Associate Role.
 • Technically supported on multiple tools, business to business.

Process Associate

Experience with data analytics , very vast and deep experience in data Analytics tools including data visualization like Tableau & Power BI as well as very good experience in SQL/MYSQL and Oracle also experience in data modeling and extract transformation & loading data through ETL tool like Informatica PowerCenter. Analysis of data analytics and business intelligence, Understand business requirements in BI context and design data models to transform raw data into meaningful insights, Ease in dealing with end-users while working on KPIs, Requirements, and detailed designs • Design, develop, test, and deploy. Ability to work with huge datasets •  Oversee firm wide data warehousing, data integration, data migration, ETL process and Business Intelligence

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