Magnifyit
AI/ML Lead
Magnifyit, Hartford, Connecticut, us, 06112
Job Responsibilities
AIML BOT development (Mandatory)
- Healthcare knowledge on claims, benefits.Python:
Expertise in Python Data Exploration and Data Science stack – Jupyter Notebook, Pandas, Matplotlib, Sci-kit Learn, etc.NLP:
Experience using Hugging Face pipelines to perform various NLP tasks such as classification, generation, entity detection, etc.LLM Application:
Hands-on experience using Llama Index or Lang chain to build semantic search, retrieval augmented generation (RAG), hybrid search systems.Prompt Engineering:
Experience using Open AI or Vertex AI or Llama APIs to design and structure the inputs to an LLM programmatically.Vector Database:
Experience using Vector Databases such as PineCone, Qdrant, Vespa, Weaviate, etc.Evaluation:
Familiarity with NLP evaluation metrics used to assess retrieval and generation quality.Cloud:
Experience using big cloud providers such as AWS, GCP, Azure to quickly deploy POCs.MongoDB:
Familiarity with MongoDB Atlas data modeling, indexing, and querying.Conversation AI:
Familiarity with conversation AI platforms such as Kore AI, RASA, Google Dialog flow, CCAI, etc.Approximate Nearest Neighbor:
Experience using libraries such as FAISS, ANNOY, etc.Advanced Prompting Techniques:
Familiarity with techniques such as Few-shot learning, Chain-of-thought, etc., and leveraging various features such as function calling, Responsible AI, etc.Vector Indexing:
Familiarity with improving vector indexing, Query Expansion, Cross-encoder reranking, Training and utilizing Embedding Adapters.
#J-18808-Ljbffr
AIML BOT development (Mandatory)
- Healthcare knowledge on claims, benefits.Python:
Expertise in Python Data Exploration and Data Science stack – Jupyter Notebook, Pandas, Matplotlib, Sci-kit Learn, etc.NLP:
Experience using Hugging Face pipelines to perform various NLP tasks such as classification, generation, entity detection, etc.LLM Application:
Hands-on experience using Llama Index or Lang chain to build semantic search, retrieval augmented generation (RAG), hybrid search systems.Prompt Engineering:
Experience using Open AI or Vertex AI or Llama APIs to design and structure the inputs to an LLM programmatically.Vector Database:
Experience using Vector Databases such as PineCone, Qdrant, Vespa, Weaviate, etc.Evaluation:
Familiarity with NLP evaluation metrics used to assess retrieval and generation quality.Cloud:
Experience using big cloud providers such as AWS, GCP, Azure to quickly deploy POCs.MongoDB:
Familiarity with MongoDB Atlas data modeling, indexing, and querying.Conversation AI:
Familiarity with conversation AI platforms such as Kore AI, RASA, Google Dialog flow, CCAI, etc.Approximate Nearest Neighbor:
Experience using libraries such as FAISS, ANNOY, etc.Advanced Prompting Techniques:
Familiarity with techniques such as Few-shot learning, Chain-of-thought, etc., and leveraging various features such as function calling, Responsible AI, etc.Vector Indexing:
Familiarity with improving vector indexing, Query Expansion, Cross-encoder reranking, Training and utilizing Embedding Adapters.
#J-18808-Ljbffr