Senior Data Scientist

Senior-level Enterprise AI Data Scientist to design, develop, and deploy enterprise-scale AI and Generative AI solutions at Flexjet, focusing on LLM powered applications, RAG pipelines, and secure enterprise data systems in Cleveland, OH (onsite).

Responsibilities

• Design and deploy enterprise-scale ML models for predictive analytics and classification tasks
• Create intelligent automation to streamline business workflows
• Build and roll out LLM powered applications, including enterprise knowledge assistants and chatbots
• Architect Retrieval-Augmented Generation pipelines
• Develop semantic search, document intelligence, and enterprise search capabilities
• Improve prompt engineering workflows and fine-tune models with domain data
• Benchmark and evaluate machine learning and LLM performance
• Work with large-scale structured and unstructured data across enterprise systems
• Design scalable data pipelines to support AI and ML workflows
• Integrate AI solutions with internal systems, APIs, and enterprise platforms
• Collaborate with data engineering teams to shape data architectures
• Deploy AI/ML models into production environments
• Implement model monitoring, performance tracking, and alerting
• Maintain model versioning, reproducibility, and lifecycle management
• Contribute to CI/CD pipelines for AI and ML deployments
• Ensure production systems are scalable, reliable, and performant
• Adopt responsible AI practices including fairness, transparency, and risk mitigation
• Ensure compliance with enterprise data governance, privacy, and security standards
• Support explainability and documentation requirements for models
• Document models, systems, and workflows thoroughly
• Translate business needs into actionable technical solutions
• Partner with product, engineering, and analytics teams to deliver AI solutions
• Communicate technical concepts clearly to non-technical stakeholders
• Contribute to system architecture decisions and design discussions
• Document workflows, design decisions, and results

Requirements

• Strong proficiency in Python and SQL
• Experience developing and deploying models across regression, classification, clustering, ensembles, and neural networks
• Solid understanding of data preprocessing, feature engineering, and model evaluation
• Prompt engineering and optimization expertise
• Experience with Retrieval-Augmented Generation (RAG)
• Experience with embeddings and vector search
• Experience evaluating and fine-tuning models
• Experience working with large, complex datasets
• Data pipelines, ETL processes, and enterprise data warehouses
• API integrations and distributed, enterprise-scale systems
• Experience building and maintaining production-ready ML systems
• Familiarity with Docker, Kubernetes, and REST APIs
• CI/CD pipelines and Git version control
• Experience with AWS, Azure, or Google Cloud

Technologies

• Python
• SQL
• Retrieval-Augmented Generation (RAG)
• LangChain
• LlamaIndex
• Hugging Face
• Docker
• Kubernetes
• REST APIs
• Git
• AWS
• Azure
• Google Cloud
• Embeddings
• Vector search
• Vector databases

Preferred Qualifications

• Experience developing LLM-powered applications in enterprise environments
• Hands-on work with RAG pipelines, embeddings, and vector databases
• Strong understanding of prompt engineering and LLM evaluation techniques
• Familiarity with LangChain, LlamaIndex, and Hugging Face frameworks
• Knowledge of MLOps practices including CI/CD, model monitoring, and lifecycle management
• Experience with Docker, Kubernetes, and containerized deployments
• Understanding of data governance, responsible AI, and model explainability