Senior Data Scientist

Flexjet•Highland Heights, OH

13d

About The Position

Flexjet 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.

Requirements

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 DevOps.
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
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
Building and maintaining production-ready ML systems
Familiarity with Docker, Kubernetes, and REST APIs
CI/CD pipelines and version control (Git)
Experience with AWS, Azure, or Google Cloud

Nice To Haves

Experience developing LLM-powered applications in enterprise environments
Hands-on experience with RAG pipelines, embeddings, and vector databases
Strong understanding of prompt engineering and LLM evaluation techniques
Familiarity with frameworks such as LangChain, LlamaIndex, and Hugging Face
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

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 results