Senior Data Scientist

Senior-Level Enterprise AI Data ScientistFlexjet 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 & ResponsibilitiesDesign and implement enterprise-scale machine learning models, including predictive and classification systemsDevelop intelligent automation solutions to streamline business workflowsBuild and deploy LLM-powered applications, such as enterprise knowledge assistants and chatbotsDesign and implement Retrieval-Augmented Generation (RAG) pipelinesDevelop solutions for semantic search, document intelligence, and enterprise search capabilitiesOptimize prompt engineering workflows and fine-tune models using domain-specific dataEvaluate and benchmark machine learning and LLM model performanceWork with large-scale structured and unstructured data sources across enterprise systemsDesign and build scalable data pipelines to support AI and machine learning workflowsIntegrate AI solutions with internal systems, APIs, and enterprise platformsPartner with data engineering teams to design and optimize data architecturesDeploy AI/ML models into production environmentsImplement model monitoring, performance tracking, and alertingMaintain model versioning, reproducibility, and lifecycle managementSupport and contribute to CI/CD pipelines for AI and ML deploymentsEnsure scalability, reliability, and performance of systems in production environmentsImplement responsible AI practices, including fairness, transparency, and risk mitigationEnsure compliance with enterprise data governance, privacy, and security standardsSupport model explainability and documentation requirementsMaintain thorough documentation of models, systems, and workflowsTranslate business needs into actionable technical solutionsWork closely with product, engineering, and analytics teams to deliver AI-driven solutionsCommunicate technical concepts and solutions clearly to non-technical stakeholdersContribute to system architecture decisions and design discussionsDocument workflows, design decisions, and resultsEducation & ExperienceBachelor'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 systemsHands-on expertise in data preprocessing, feature engineering, and model evaluationExperience working with APIs, large datasets, and enterprise systemsRequired Technical Skills & QualificationsProgramming: Strong proficiency in Python and SQLExperience developing and deploying models (regression, classification, clustering, ensembles, neural networks)Strong understanding of data preprocessing, feature engineering, and model evaluationPrompt engineering and optimizationRetrieval-Augmented Generation (RAG)Embeddings and vector searchModel evaluation and fine-tuningExperience working with large, complex datasetsData pipelines, ETL processes, and enterprise data warehousesAPI integrations and distributed/enterprise-scale systemsBuilding and maintaining production-ready ML systemsFamiliarity with Docker, Kubernetes, and REST APIsCI/CD pipelines and version control (Git)Experience with AWS, Azure, or Google CloudPreferred QualificationsExperience developing LLM-powered applications in enterprise environmentsHands-on experience with RAG pipelines, embeddings, and vector databasesStrong understanding of prompt engineering and LLM evaluation techniquesFamiliarity with frameworks such as LangChain, LlamaIndex, and Hugging FaceKnowledge of MLOps practices, including CI/CD, model monitoring, and lifecycle managementExperience with Docker, Kubernetes, and containerized deploymentsUnderstanding of data governance, responsible AI, and model explainability