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