Machine Learning Intern

5 days ago

Remote, India BreakoutAI Full time

**Responsibilities**:
As a **Knowledge Graph Intern**, your key responsibilities will include:

- **Data Collection & Scraping**: Use web scraping techniques and tools (e.g., BeautifulSoup, Scrapy) to collect publicly available information from websites, reports, social media, and other sources about companies and their key attributes.
- **Named Entity Recognition (NER)**: Implement NER techniques using libraries like SpaCy or NLTK to identify entities such as companies, people, products, and relationships from the extracted data.
- **Knowledge Graph Construction**: Structure and build knowledge graphs using **Neo4j** or similar graph databases to model entities and their relationships.
- **Vector Database Integration**: Work with vector databases (e.g., Pinecone, FAISS) to incorporate semantic search capabilities and enable efficient information retrieval from large datasets.
- **Data Structuring & Annotation**: Clean and organize unstructured data to fit into a knowledge graph structure, ensuring the relationships between entities are accurately represented.
- **Python Scripting**: Write Python scripts to automate data extraction, processing, and integration tasks, including the development of reusable code modules.
- **Collaboration**: Work closely with the data science team to optimize graph-based queries and integrate knowledge graphs into larger machine learning models.
- **Documentation**: Maintain clear and detailed documentation of the workflows, tools used, and processes followed for future reference and scalability.

**Job Type**: Internship
Contract length: 4 months

Pay: From ₹300,000.00 per year

**Benefits**:

- Paid time off
- Work from home

Schedule:

- Evening shift
- Weekend availability

Supplemental Pay:

- Commission pay
- Performance bonus

Application Question(s):

- Do you have experience with graph databases such as Neo4j or Amazon Neptune? If yes, please briefly describe a project you've worked on.
- What is your experience with Named Entity Recognition (NER) and relationship extraction in NLP tasks?
- What machine learning models or techniques have you used for tasks such as entity linking or automated knowledge extraction?
- Have you worked with NLP libraries or frameworks? If yes, which ones (e.g., spaCy, NLTK, Hugging Face)?

Work Location: Remote

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