Role: Senior Data Scientist - GenAI / RAG
Location: Houston, TX -preferred Or Dallas TX or Santa Clara CA (Onsite - 3 Days)
Type: Full-Time
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
We are looking for a Senior Data Scientist with a strong traditional ML/DS background and hands-on experience in GenAI (LLMs, RAGs, Agentic workflows). The candidate should not be purely academic or junior; we need someone with practical implementation experience and the ability to interact confidently with customers. Strong communication and product-facing exposure are equally important.
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
Key Responsibilities:
- Develop and implement machine learning algorithms to solve complex business problems.
- Analyze large datasets to generate insights and inform decision-making processes.
- Collaborate with product managers and engineers to integrate data science solutions into enterprise products.
- Communicate findings and recommendations effectively to technical and non-technical stakeholders.
- Stay current with the latest advancements in data science and machine learning technologies.
Skills and Tools Required:
- Strong proficiency in programming languages such as Python or R.
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Solid understanding of statistical analysis techniques and data modeling.
- Proficiency in data visualization tools (e.g., Tableau, Power BI).
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Ability to work with databases and query large datasets using SQL.
- Strong problem-solving skills and the ability to think critically.
- Excellent communication and collaboration skills.
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).
About the Team:
You will be part of a dynamic team of data scientists, analysts, and product managers dedicated to creating innovative solutions for enterprise-level clients. The team thrives on collaboration, leveraging diverse expertise to tackle complex challenges. A culture of continuous learning and knowledge sharing is fostered, allowing team members to stay up-to-date with the latest industry trends and technologies.
You are Responsible for:
Developing and deploying machine learning models to solve business problems.
Analyzing complex datasets to extract actionable insights that contribute to product development.
Collaborating with cross-functional teams to integrate data science solutions into existing products and services.
Providing mentorship and guidance to junior data scientists and fostering a collaborative environment.
To succeed in this role - you should have the following:
Strong experience in machine learning algorithms and statistical modeling techniques.
Proficiency in programming languages such as Python or R, along with data manipulation libraries.
Experience with big data technologies like Hadoop, Spark, or similar platforms.
Excellent analytical skills with the ability to communicate complex findings to non-technical stakeholders.
A degree in a quantitative field, such as Computer Science, Statistics, Mathematics, or related disciplines.
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