Senior Data Scientist

About the role

Role: Senior Data Scientist – GenAI / RAG

Location: Houston, TX (Onsite – 3 Days)

Type: Full-Time

Focus: 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.

What the Hiring Team is Looking For

Traditional Data Scientists who have evolved into GenAI/Agentic AI solutions.

Candidates who can discuss end-to-end problem solving, feature engineering, predictive analytics, model selection, and solution design.

Hands-on experience designing and implementing RAG, LLM, and Agentic AI solutions.

Strong understanding of business use cases and ability to interact with customers and stakeholders.

Ability to explain architecture decisions, technical trade-offs, and implementation approaches.

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)

Key Responsibilities

Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces

Work closely with client data science teams (ML/DL ecosystems)

Develop GenAI-based enterprise knowledge solutions

Collaborate directly with stakeholders and customers

Tech Environment

AWS ecosystem

Snowflake (data platform)