AI/ML Engineer

Overview

On Site
BASED ON EXPERIENCE
Contract - Independent
Contract - W2

Skills

Machine Learning (ML)
Customer experience
Python
Data
Data Science
Jupyter
Pandas
matplotlib
Semantic search
Prompt Engineering
Vertex
Design
Database
Vector Databases
Natural language processing
Evaluation
Metrics
Cloud computing
Amazon Web Services
Google Cloud Platform
Google Cloud
Microsoft Azure
MongoDB
ATLAS
Data modeling
Artificial intelligence
SANS
Training
Adapter
Technical direction

Job Details

Role: AI/Client Engineer
Location: Hartford, CT 06103 (Day 1 Onsite) - Only Local candidate
Duration: Long Term contract


Skill Matrix to be filled by Candidates:
Mandatory Skills Years of Experience Year Last Used Rating Out of 10
Google Vertex AI
Python Coding
NLP
Google Cloud Platform
vector indexing
LLM

  • 4-5 Years of AI/Client 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, Google Cloud Platform, 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.
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