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
Flexjet is an equal-opportunity employer. We aim to choose individuals who have the highest integrity; those who personify genuine concern for customers and fellow employees alike. More than anything, we look for individuals who grasp the importance of trust in an employer/employee relationship.
