Skills required for a Gen AI job with NLP and ML

Came across this JD. Giving below the skills required in detail.

Position Name :- GEN AI Engineer

Job Responsibilities :-

5+ years experience in designing, developing and deploying production-grade machine learning solutions in NLP (NLTK, Spark NLP, spaCy, HuggingFace, Flair, NLTK, etc) for real-world business problems.

Worked in NLP model architectures and algorithms such as BERT.

Experience in LLMs/Open Source LLMs and Langchain frameworks.

Combination of deep technical skills and business sense, to interface with all levels and disciplines within an organization.

Excellent written and verbal communication skills to explain complex research to both technical and non-technical audiences.

Self-motivated individual that thrives in a dynamic environment.

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Key skills required from a Gen AI professional with NLP and ML. Here are examples of solutions that would likely be expected:

Skills required:

1. Machine learning expertise, particularly in Natural Language Processing (NLP)

2. Proficiency in NLP libraries and frameworks

3. Experience with production-grade deployments

4. Ability to solve real-world business problems

5. Programming skills (likely Python)

Examples of expected solutions:

1. Text Classification:

Developing models to automatically categorize customer feedback, support tickets, or product reviews. This could involve using pre-trained models from HuggingFace and fine-tuning them on company-specific data.

2. Named Entity Recognition (NER):

Creating a system to extract important information like names, locations, and organizations from unstructured text data. This might be implemented using spaCy or Flair for tasks such as identifying key entities in legal documents or extracting product mentions from social media posts.

3. Sentiment Analysis:

Building models to determine the sentiment (positive, negative, neutral) of customer interactions or social media mentions. This could involve using NLTK for preprocessing and a deep learning model for classification.

4. Text Summarization:

Developing an automated system to generate concise summaries of long documents, such as research reports or news articles. This might use transformer-based models from HuggingFace.

5. Question Answering:

Creating a system that can automatically answer customer queries based on a knowledge base. This could involve fine-tuning a model like BERT or RoBERTa on company-specific data.

6. Language Translation:

Implementing a machine translation system for multi-language customer support or content localization. This might involve using pre-trained models and adapting them to specific domain terminology.

7. Topic Modeling:

Developing a system to automatically identify main topics in large text corpora, such as customer feedback or internal documents. This could use techniques like Latent Dirichlet Allocation (LDA) implemented in libraries like Gensim.

8. Text Generation:

Creating models for tasks like automated report writing or content creation. This might involve fine-tuning large language models on specific domains or tasks.

9. Information Extraction:

Building systems to automatically extract structured information from unstructured text, such as pulling key details from invoices or contracts.

10. Scalable NLP Pipeline:

Designing and implementing an end-to-end NLP pipeline using Spark NLP that can process large volumes of text data efficiently, possibly integrating multiple NLP tasks.

These solutions would be expected to be deployed in production environments, likely involving containerization (e.g., Docker), cloud services, and MLOps practices for monitoring and maintaining model performance over time.

Would you like me to elaborate on any of these points or provide more specific examples?