60 Flexjet Data Jobs Hiring Near You

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