Full Time, PermanentRole Category:
Data Science & Machine Learning
EducationUG:
Any Graduate, B.Tech/B.E. in Any SpecializationPG:
Any Postgraduate
Job description
This mail is with reference to your profile on Job board. We have wonderful opportunity for AIML Engineer Professionals
If you are interested in the below opening with Kaya Global*. Please send your latest resume in to Maryv@Kayatech.com with the following information,
Total Experience: –
Rel Experience (Python):-
Rel Experience (LLM):-
Rel Experience (RAG Systems):-
Rel Experience (Vector Database):-
Rel Experience (NLP):-
Rel Experience (Langsmith):-
Current Company: –
Current CTC:-
Expected CTC:-
Notice Period/Earliest joining time:-
Please find Job Description below:
Experience: 2 yrs to 4 yrs
Location: Bangalore/Chennai, India
Required Tools & Technologies:
• Programming languages: Python
• Machine learning and NLP libraries and frameworks: TensorFlow, PyTorch, JAX, NLTK • Vector database libraries: Faiss, Annoy, Pinecone, pgvector, Milvus.
• Embedding libraries: Gensim, spaCy • Search and conversational AI systems libraries: Langchain, haystack
• Transformer libraries: Hugging Face Transformers, AllenNLP
• Cloud computing platforms: AWS, Azure, GCP
• Other tools: Jupyter Notebook, Git, Docker
• Chatbot development framework (e.g., Rasa, Rasa NLU, Botpress)
• Tools for building and managing production-grade LLM apps: Langsmith
• Tool for monitoring and maintaining LLMs in production: Weights & Biases, CometML.
Key Qualifications:
- Train and deploy large language models (LLMs) using techniques such as vector
- databases (e.g., Faiss, pgvector, Pinecone, Milvus), embeddings
- (e.g., Gensim, spaCy), transformers (e.g., Hugging Face Transformers, AllenNLP), andnatural language processing (NLP) algorithms and techniques.
- Optimize LLM performance by tuning hyperparameters and using techniques such as knowledge distillation and fine-tuning.
- Evaluate and improve accuracy of semantic and hybrid search.
- Develop and test data pipelines for training and evaluating LLMs using machine learning
- frameworks such as TensorFlow, PyTorch, and JAX.
- Develop and test data extraction from various data sources, pre-process, transform and
- loading into vector databases via different chunking strategies.
- Collaborate with the software development team to integrate LLMs into the chatbot using a chatbot development framework (e.g., Rasa, Rasa NLU, Botpress).
- Monitor and maintain LLM performance in production using a tool for monitoring and maintaining LLMs in production (e.g., Weights & Biases, CometML).
Thanks & Regards,
Mary
Application Link : CLICK HERE
