Senior Data Scientist
On-siteCleveland, Ohio, United States
Job Summary
Design and deploy enterprise-scale machine learning models, including predictive systems and Retrieval-Augmented Generation (RAG) pipelines, to automate workflows and enhance decision-making. Build LLM-powered applications such as internal knowledge assistants, document processing systems, and semantic search tools using domain-specific data. Optimize prompt engineering, fine-tune models, and integrate AI solutions with internal systems, APIs, and enterprise platforms while collaborating with data engineering and product teams. Evaluate model performance, maintain versioning and lifecycle management, and ensure compliance with data governance, privacy, and security standards.
Required Qualifications
- 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
Desired 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
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