Role: Senior Data Scientist – GenAI / RAG
Location: Houston, TX (Onsite – 3 Days)
Fulltime
We need candidates with real hands-on experience in Agentic AI and Traditional Data Science, not candidates who have only recently started exploring GenAI/LLMs. Continuous production implementation experience is preferred.
Mandatory Skills
- Strong Data Science background
- Strong Machine Learning implementation experience
- Hands-on LLM & RAG architecture and implementation
- Experience with Agentic AI concepts and workflows
- Strong client-facing / consulting and stakeholder communication skills
- Exposure to Deep Learning concepts and implementations
- Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, or Vertex AI
Job Description
Strong hands-on experience in Agentic AI and Multi-Agent Systems
Experience with LangGraph, LangChain, MCP (Model Context Protocol), tool calling, agent orchestration
Strong RAG implementation experience including Vector Databases, Hybrid Retrieval, Reranking, Knowledge Graphs
Hands-on experience with AWS Bedrock and enterprise GenAI solutions
Strong Python and SQL skills
Solid Traditional Data Science / Machine Learning background:
- Classification
- Regression
- Forecasting
- Anomaly Detection
- Feature Engineering
- Model Evaluation
Experience with XGBoost, CatBoost, Random Forest, Deep Learning frameworks (PyTorch/TensorFlow)
Experience designing and deploying production AI/ML systems
Understanding of MLOps / LLMOps, model monitoring, evaluation, observability, and retraining pipelines
Ability to translate business problems into ML or Agentic AI solutions
Experience with LLM Evaluation, Hallucination Detection, Groundedness and Retrieval Quality metrics
Exposure to Databricks, Spark, Vector Databases, APIs, Cloud Platforms (AWS preferred)
Preferred Qualifications:
- A master's or Ph.D. in computer science, statistics, mathematics, or a related field.
- Experience in the tech industry or with enterprise-level software products.
- Understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud).