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

𝗔𝘀 𝗮𝗽𝗽𝘀 𝗺𝗼𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗱 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝘀𝗰𝗮𝗹𝗲, 𝗼𝗻𝗲 𝘁𝗿𝘂𝘁𝗵 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁.
There is no longer a single database that fits every use case.

It’s not SQL vs NoSQL anymore.
It’s about choosing the right database for your data and your workload.

𝗟𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁 👇

𝟭. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 (𝗦𝗤𝗟) 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Structured, reliable, and great for transactions.
𝗨𝘀𝗲𝗱 𝗶𝗻: Banking, ERP, Order Management
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: MySQL, PostgreSQL, SQL Server


𝟮. 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Designed for analytics and lightning-fast aggregations.
𝗨𝘀𝗲𝗱 𝗶𝗻: Data Warehousing, BI, OLAP
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Redshift, ClickHouse, HBase


𝟯. 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Handle data that changes over time — metrics, events, IoT signals.
𝗨𝘀𝗲𝗱 𝗶𝗻: Monitoring, IoT, Real-Time Analytics
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: InfluxDB, TimescaleDB, QuestDB


𝟰. 𝗜𝗻-𝗠𝗲𝗺𝗼𝗿𝘆 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Everything runs in memory → ultra-low latency responses.
𝗨𝘀𝗲𝗱 𝗶𝗻: Fraud Detection, Real-Time Apps, Caching
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Redis, SAP HANA, Ignite

𝟱. 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Perfect when relationships between data matter most.
𝗨𝘀𝗲𝗱 𝗶𝗻: Social Networks, Recommendations, Knowledge Graphs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Neo4j, JanusGraph, Cosmos DB


𝟲. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Flexible and great for unstructured or semi-structured data.
𝗨𝘀𝗲𝗱 𝗶𝗻: CMS, User Profiles, Mobile Apps
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: MongoDB, Couchbase, Firestore


𝟳. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ The new stars of AI — store embeddings for smart, semantic search.
𝗨𝘀𝗲𝗱 𝗶𝗻: Generative AI, RAG, Similarity Search
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Pinecone, Milvus, Weaviate


𝟴. 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Store data as objects that match your code directly.
𝗨𝘀𝗲𝗱 𝗶𝗻: CAD/CAM, Complex Engineering Systems
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: db4o, ZODB

𝟵. 𝗕𝗹𝗼𝗰𝗸𝗰𝗵𝗮𝗶𝗻 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Decentralized, tamper-proof, and transparent.
𝗨𝘀𝗲𝗱 𝗶𝗻: Supply Chain, Identity, Provenance Tracking
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: BigchainDB, CovenantSQL

𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀: 

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄: 
Elise Victor, PhD
Yuma .
Sol Rashidi, MBA
Cornellius Y.
Sairam Sundaresan
Aishwarya Srinivasan

♻️ Please 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝗦𝗵𝗮𝗿𝗲 to help others stay informed

Want to stay updated on the latest AI Tools and AI Agents? Join my free AI WhatsApp community 👇
https://lnkd.in/epHZYb-j

#AI #SQL #NoSQL

#AIForEveryone | Learn AI with Me | AI Tools • AI Agents • AI News | 160k+ Followers | Ex-Deloitte & PwC

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗮 𝗴𝗲𝗺 𝗳𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲 𝘄𝗮𝗻𝘁𝗶𝗻𝗴 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲.

If you’ve been wondering how to actually build AI Agents, this could be your real starting point.

This new “AI Agents for Beginners” playlist is your hands-on guide from 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 → 𝗰𝗼𝗱𝗲.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻:

↳ What AI Agents are & how they work
↳ Framework Semantic Kernel
↳ Design & planning patterns for agents
↳ Tool use, RAG & multi-agent systems
↳ Deploying, improving & adding agent memory

Want to master AI Agents and AI Tools? Check this: https://lnkd.in/eayn5Cnt


𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗺𝗼𝗿𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀! 👇

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄:
Shivani Virdi
Andreas Horn
Armand Ruiz
Michael Kisilenko
Lee Boonstra
Miku Jha

#AIAgents #MicrosoftDeveloper #AgenticAI

When you look at AI through the right lens, it falls into three clear buckets.

So I created the simplest breakdown ever. Save this.

We’re living through three eras of AI: Traditional, Generative, and Agentic


𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 👇

𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 
↳ Helps you analyze, classify, and detect patterns.
↳ No creativity. Just logic, data, and rules.
✔ Predictive analytics
✔ Classification systems
✔ Anomaly detection


𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜
↳ This is where ChatGPT and LLMs changed the game.
↳ AI doesn’t just process information, it creates new content.
✔ Content generation
✔ Workflow automation
✔ Knowledge systems (RAG)

𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 
↳ AI that acts, executes, and makes decisions on its own.
↳ Not just answers… real outcomes.
✔ Agents and tool use
✔ Multi-agent orchestration
✔ AI product integration


If you're learning AI in 2025-26, don’t stop at Generative AI.
Aim for building Agents, not just prompts.

The world is evolving from
"AI tells you what to do" to "AI does it for you."

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄: 
Shivani Virdi
Andreas Horn
Armand Ruiz
Michael Kisilenko
Lee Boonstra

𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗳𝗿𝗲𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀: 👇

♻️ Please 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝗦𝗵𝗮𝗿𝗲 to help others stay informed

Want to stay updated on the latest AI Tools and AI Agents? Join my free AI WhatsApp community 👇
https://lnkd.in/epHZYb-j


#AI #ML #GenAI

Thinkitive Technologies

Data Analyst

HiCounselor

- Created test objectives with data analysis and data science project goals using Python and SQL, ensuring focused testing efforts and project alignment. 
- Developed Python test cases covering diverse scenarios, reducing testing time by 80%. 
- Produced clear project content to enhance communication and documentation, resulting in a 50% reduction in misunderstandings. 
- Evaluated technical resumes to select qualified candidates for data-centric roles, increasing hiring efficiency by 40%. 
- Collaborated seamlessly with diverse teams, fostering communication between technical and non-technical stakeholders for project success.

Software Developer (Data Analytics)

- Develop and maintain programs using C++, C#, and Java programming languages
- Write code to test user-written programs in IDEs (Integrated Development Environments)
- Analyze and optimize code for better performance and efficiency

Software Developer Intern

KNS Infrastructure

Intern

👋 Welcome to my LinkedIn profile! With over a year of hands-on experience in software development and data analysis, I am passionate about leveraging technology to drive insightful solutions and empower data-driven decision-making.

Proficiencies:

🌟 SQL: Proficient in writing complex queries to extract, manipulate, and analyze data from databases.

🐍 Python: Skilled in developing data-driven applications and performing advanced analytics using Python libraries.

📊 Power BI: Experienced in creating interactive dashboards and reports to visualize data and facilitate decision-making.

📈 Excel: Expertise in utilizing Excel for data analysis, modeling, and automation to streamline processes and enhance productivity.

Career Focus:

As a software developer, I have successfully contributed to projects that blend my technical skills with a strong foundation in data analysis. My experience includes optimizing database performance, building robust applications, and crafting compelling data stories that drive business insights.

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Aditya Sharma

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