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Epicareer might not working properly because your browser javascript is turned off.

What is JavaScript?

JavaScript is like the magic behind making websites interactive. Basically, it makes Epicareer more enjoyable and engaging.
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Find tutorial for your browser below :
  1. Make sure the webpage is open.
  2. In the Safari app on your Mac, choose Safari > Preferences, then click Security.
  3. Make sure the Enable JavaScript checkbox is selected.
  4. Click Websites.
  5. On the left, click Content Blockers.
  6. Make sure Off is chosen in the pop-up menu next to the website.
  7. On the left, click Pop-up Windows.
  8. Make sure Allow is chosen in the pop-up menu next to the website.
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  1. Open Chrome on your computer.
  2. Click. then Settings.
  3. Click Privacy and Security.
  4. Click Site settings.
  5. Click JavaScript.
  6. Select Sites can use Javascript.
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  1. If you're running Windows OS, in the Firefox window, click Tools > Options.
  2. Tip: If you're running Mac OS, click the Firefox drop-down list > Preferences.
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  1. Click the "Settings and more" icon at the top right of the Edge browser window.
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  4. Scroll down and click on JavaScript.
  5. In the JavaScript window, toggle the slider to "on" or "allowed."
  6. Close the Settings tab.
  7. Refresh the page.
Visit Site here
  1. Go to Settings.
  2. Click Advanced in the left sidebar, and click Privacy & security.
  3. Under Privacy and security, click Site settings.
  4. Click JavaScript.
  5. At the top, turn on or off Allowed (recommended).
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Data Scientist

Salary undisclosed
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Original
Simplified
  1. Python expert ; APIs
  2. GHA ; YAML ; build/deploy pipelines ; Docker ; AWS
  3. LLMs ; Chatbots ; OpenAI ; tool calling ; LangChain ; LangGraph ; Agentic architecture ; RAG
  4. Java ; Spring
  5. React ; JavaScript
  • LLM, RAG & Agentic Framework

Key Responsibilities:

Design and implement Retrieval-Augmented Generation (RAG) pipelines using LangChain and LangGraph.

Integrate AWS Open Source Vector Databases (e.g., OpenSearch with KNN plugin or other OSS-compatible vector stores).

Handle complex query chaining, prompt orchestration, and data retrieval using custom agents and tools.

Work on graph-based knowledge representation and retrieval using technologies like Neo4j or Stardog, or similar (preferred).

Collaborate with data science and engineering teams to enable end-to-end use case delivery.

Optimize performance for multi-document, multi-hop reasoning over structured/unstructured data.

Required Skills:

  • Strong hands-on experience with Python, LangChain and LangGraph
  • Experience implementing RAG pipelines using open-source vector stores
  • Familiarity with Graph Databases and GraphQL
  • Good understanding of LLMs, embeddings, and prompt engineering
  • Experience with AWS services (S3, Lambda, ECS/EKS, etc.)
  • Familiarity with FastAPI or Flask for API deployments
  • Good to heve Version control and DevOps exposure (Git, Docker, CI/CD pipelines)
  1. Python expert ; APIs
  2. GHA ; YAML ; build/deploy pipelines ; Docker ; AWS
  3. LLMs ; Chatbots ; OpenAI ; tool calling ; LangChain ; LangGraph ; Agentic architecture ; RAG
  4. Java ; Spring
  5. React ; JavaScript
  • LLM, RAG & Agentic Framework

Key Responsibilities:

Design and implement Retrieval-Augmented Generation (RAG) pipelines using LangChain and LangGraph.

Integrate AWS Open Source Vector Databases (e.g., OpenSearch with KNN plugin or other OSS-compatible vector stores).

Handle complex query chaining, prompt orchestration, and data retrieval using custom agents and tools.

Work on graph-based knowledge representation and retrieval using technologies like Neo4j or Stardog, or similar (preferred).

Collaborate with data science and engineering teams to enable end-to-end use case delivery.

Optimize performance for multi-document, multi-hop reasoning over structured/unstructured data.

Required Skills:

  • Strong hands-on experience with Python, LangChain and LangGraph
  • Experience implementing RAG pipelines using open-source vector stores
  • Familiarity with Graph Databases and GraphQL
  • Good understanding of LLMs, embeddings, and prompt engineering
  • Experience with AWS services (S3, Lambda, ECS/EKS, etc.)
  • Familiarity with FastAPI or Flask for API deployments
  • Good to heve Version control and DevOps exposure (Git, Docker, CI/CD pipelines)