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. 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