Senior Data Scientist – Generative AI
US, EST or CST time zones preferred or required depending on coordination needsFull-TimeSenior
Salary117,600 - 161,700 USD per year
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Job Details
- Experience
- 5+ years of experience, or Master’s degree with 3+ years
- Required Skills
- PythonGCPSnowflakeAzureSparkComplianceDatabricksNLPPrompt EngineeringLLMGenerative AILangChain
Requirements
- Bachelor’s degree with 5+ years of experience, or Master’s degree with 3+ years in data science, computer science, engineering, or a related field
- Strong proficiency in Python and modern software development practices for AI systems
- Deep experience working with NLP and text-based applications, including transformer models and embeddings
- Hands-on expertise with LLM architectures, prompt engineering, and frameworks such as LangChain, LangGraph, AutoGen, or CrewAI
- Experience building retrieval-augmented generation systems and agent-based AI workflows
- Familiarity with cloud and data platforms such as Azure, GCP, Databricks, or Snowflake
- Experience deploying and supporting AI/ML systems in production environments
- Strong understanding of data privacy, governance, and compliance requirements
- Excellent communication skills with the ability to collaborate across technical and non-technical teams
- Experience working in EST or CST time zones preferred or required depending on coordination needs
Responsibilities
- Design, develop, and deploy LLM-based applications for conversational intelligence and insight generation
- Build multi-step AI workflows including retrieval-augmented generation, orchestration, routing, and tool-using agent patterns
- Define and implement evaluation frameworks for LLM outputs, including quality, safety, reliability, and business value
- Work with large-scale structured and unstructured datasets using platforms such as Spark, Databricks, and Snowflake
- Partner with engineering teams to transition GenAI prototypes into scalable production systems
- Develop clean, maintainable Python code and contribute to engineering best practices including version control, documentation, and code reviews
- Ensure adherence to enterprise standards for security, privacy, compliance, and responsible AI governance
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