Senior Data Scientist

# Senior Data Scientist Flexjet Cleveland , OH, US2 days agoJob description POSITION SUMMARYFlexjet is seeking a Senior-Level Enterprise AI Data Scientist to design, develop, and deploy enterprise-scale AI and Generative AI solutions that improve productivity, automate workflows, and enhance decision-making across the organization.

This role focuses on building LLM-powered enterprise applications, such as internal knowledge assistants, document processing systems, and workflow automation tools. The ideal candidate has hands-on experience with machine learning, large language models (LLMs), Retrieval-Augmented Generation (RAG), and enterprise data systems.

Collaborate with data engineers, software engineers, product teams, and business stakeholders to build secure, scalable, and production-ready AI solutions that align with enterprise governance and compliance standards.

DUTIES & RESPONSIBILITIES
· Design and implement enterprise-scale machine learning models, including predictive and classification systems
· Develop intelligent automation solutions to streamline business workflows
· Build and deploy LLM-powered applications, such as enterprise knowledge assistants and chatbots
· Design and implement Retrieval-Augmented Generation (RAG) pipelines
· Develop solutions for semantic search, document intelligence, and enterprise search capabilities
· Optimize prompt engineering workflows and fine-tune models using domain-specific data
· Evaluate and benchmark machine learning and LLM model performance
· Work with large-scale structured and unstructured data sources across enterprise systems
· Design and build scalable data pipelines to support AI and machine learning workflows
· Integrate AI solutions with internal systems, APIs, and enterprise platforms
· Partner with data engineering teams to design and optimize data architectures
· Deploy AI/ML models into production environments
· Implement model monitoring, performance tracking, and alerting
· Maintain model versioning, reproducibility, and lifecycle management
· Support and contribute to CI/CD pipelines for AI and ML deployments
· Ensure scalability, reliability, and performance of systems in production environments
· Implement responsible AI practices, including fairness, transparency, and risk mitigation
· Ensure compliance with enterprise data governance, privacy, and security standards
· Support model explainability and documentation requirements
· Maintain thorough documentation of models, systems, and workflows
· Translate business needs into actionable technical solutions
· Work closely with product, engineering, and analytics teams to deliver AI-driven solutions
· Communicate technical concepts and solutions clearly to non-technical stakeholders
· Contribute to system architecture decisions and design discussions
· Document workflows, design decisions, and resultsEDUCATION & EXPERIENCE
· Bachelor's or master's degree in computer science, Information Technology, Data Science, or a related field, or an equivalent combination of education, training, and relevant professional experience.
· 5+ years of experience in Data Science, Machine Learning, and AI software engineering, machine learning engineering, platform engineering, MLOps, or Dev Ops.
· Experience building and deploying production ML systems
· Hands-on expertise in data preprocessing, feature engineering, and model evaluation
· Experience working with APIs, large datasets, and enterprise systems REQUIRED TECHNICAL SKILLS & QUALIFICATIONS
· Programming:
Strong proficiency in Python and SQL
· Experience developing and deploying models (regression, classification, clustering, ensembles, neural networks)
· Strong understanding of data preprocessing, feature engineering, and model evaluation
· Prompt engineering and optimization
· Retrieval-Augmented Generation (RAG)
· Embeddings and vector search
· Model evaluation and fine-tuning
· Experience working with large, complex datasets
· Data pipelines, ETL processes, and enterprise data warehouses
· API integrations and distributed/enterprise-scale systems
· Deployment &

Infrastructure:
· Building and maintaining production-ready ML systems
· Familiarity with Docker, Kubernetes,…