Google Vertex AI Engineer

Job Title: Google Vertex AI EngineerLocation: Hartford, CT (Day1 Onsite)Job Description:• 4-5 Years of AI/ML experience.• 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.• Familiarity with MongoDB Atlas data modeling, indexing, and querying.• Familiarity with conversation AI platforms such as Kore AI, RASA, Google Dialog flow, CCAI, etc• Experience using Approximate Nearest Neighbor libraries such as FAISS, ANNOY, etc.• Familiarity with advanced Prompting techniques such as Few-shot learning, Chain-of-thought, etc. and leverage various features such as function calling, Responsible AI, etc.• Familiarity with improving the vector indexing, Query Expansion, Cross-encoder reranking, Training and utilizing Embedding Adapters.Job Title: Google Vertex AI EngineerLocation: Hartford, CT (Day1 Onsite)Job Description:• 4-5 Years of AI/ML experience.• 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.• Familiarity with MongoDB Atlas data modeling, indexing, and querying.• Familiarity with conversation AI platforms such as Kore AI, RASA, Google Dialog flow, CCAI, etc• Experience using Approximate Nearest Neighbor libraries such as FAISS, ANNOY, etc.• Familiarity with advanced Prompting techniques such as Few-shot learning, Chain-of-thought, etc. and leverage various features such as function calling, Responsible AI, etc.• Familiarity with improving the vector indexing, Query Expansion, Cross-encoder reranking, Training and utilizing Embedding Adapters.