AI Data Scientist | GEN AI and LLMs

Job Title:

AI Data Scientist GenAI / LLM Engineer

Location: San Jose, CA
Experience

Required:

8+ Years
Employment Type: Contract

We are looking for a highly experienced AI Data Scientist with strong expertise in Generative AI, Large Language Models (LLMs), NLP, Machine Learning, and scalable AI system design. The ideal candidate should have hands‑on experience building end‑to‑end AI/ML solutions, deploying production‑grade applications, and working with modern GenAI frameworks and vector databases.

Required

Skills & Experience:

Generative AI / LLM Technologies:

• Strong Python programming expertise

• Hands‑on experience with:

  • Lang Chain

  • Lang Graph

  • RAG (Retrieval‑Augmented Generation)

  • RAGAS

  • Lang Smith

  • Deep Eval

• Strong understanding of:

  • Semantic Search

  • Reranking techniques

  • Vector Databases

  • Prompt Engineering

  • LLM Tool Calling / Function Calling

• Experience evaluating and optimizing LLM performance and AI pipelines

NLP (Natural Language Processing):

• BERT

• SBERT

• Transformers

• Word2
  
  Vec

• Glo Ve

• Fast Text

• RNNs / LSTMs

• DIET Classifier (RASA)

Machine Learning:

• XGBoost

• Random Forest

• Gradient Boosting Models (GBM)

• Neural Networks

• Logistic Regression

• Time Series Models

Engineering & Deployment:

• Django

• REST APIs

• FastAPI

• Web Sockets

• Microservices Architecture

• Kubernetes

• Helm

• Git

• AWS Services including:

  • DynamoDB

  • Open Search

Additional Requirements:

• Strong experience with Vector Databases
• Experience building APIs and Microservices
• Ability to design standalone scalable AI systems and architecture
• Proven experience delivering end‑to‑end implementations in production environments
• Strong communication and problem‑solving skills

Preferred Candidate Profile:

• 8+ years of overall industry experience
• Strong background in AI/ML production systems
• Experience working in enterprise‑scale environments
• Ability to explain architecture, workflows, and deployment strategies clearly during discussions/interviews