Strong background in Natural Language Processing (NLP) techniques such as text classification, sentiment analysis, and entity recognition.
Proficiency in Retrieval-Augmented Generation (RAG) techniques and hands-on experience with vector databases like Weaviate or Pinecone.
Advanced Python programming skills with the ability to build and implement complex machine learning solutions.
At least 1 year of experience developing applications using Large Language Models (LLMs) (e.g., OpenAI, Anthropic).
Skilled in prompt engineering, function calls, and building conversational AI solutions.
Familiarity with zero-shot, few-shot learning, and LLM fine-tuning.
Practical experience with libraries such as LangChain, HuggingFace, and other Generative AI tools.
Experience with CI/CD, containerization (Docker), and cloud infrastructure (Azure/AWS/GCP).
Basic understanding of deep learning architectures like CNNs, RNNs, and transformers.
Experience with local LLMs (e.g., LLaMA, Mistral, GPT-J) and vector databases (e.g., Pinecone, FAISS).
Solid programming skills in Python; exposure to JavaScript or full-stack is a plus.
Excellent problem-solving and logical reasoning abilities.
Knowledge of RAG (Retrieval-Augmented Generation) and Prompt Engineering.
Experience with model fine-tuning, zero-shot/few-shot learning techniques.
Familiar with HuggingFace, LangChain, and model deployment tools.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Analytics - OtherRole: Data Science & Analytics - OtherEmployement Type: Full time