What are the responsibilities and job description for the Senior Data Scientist position at Flexjet?
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
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
- 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
- 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
- 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