Senior Data Scientist

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.

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
• Evaluate and benchmark machine learning and LLM model performance
• Optimize prompt engineering workflows and fine-tune models using domain-specific data
• 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
• Understanding of data governance, responsible AI, and model explainability