Hugging Face Jobs in Akron, OH (NOW HIRING)

POSITION SUMMARY
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.
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 results
EDUCATION & 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 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
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, and REST APIs
� CI/CD pipelines and version control (Git)
� Experience with AWS, Azure, or Google Cloud
PREFERRED QUALIFICATIONS
� 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