Responsibilities:

• Lead, mentor, recruit, and train a best-in-class data engineering and AI/ML team
• Create and maintain optimal and scalable data pipeline architecture and solutions
• Model reporting schemas for optima retrieval performance and insights
• Enhance the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Amazon Redshift and other data warehouses
• Create and implement data tools for analytics and data science team members that assist them in building and optimizing our product into an innovative industry leader
• Lead the design, development, and fine-tuning of large language models (e.g., GPT, BERT, Claude) for content generation and prediction, incorporating functional context data as appropriate
• Develop AI models using techniques such as supervised, unsupervised, reinforcement learning, and transfer learning
• Design robust AI system architectures that can scale across large datasets and complex workflows. Implement best practices for deploying AI models into production
• Stay at the forefront of AI and machine learning research, particularly in the areas of LLMs, NLP, computer vision, and deep learning. Experiment with new algorithms, frameworks, and tools to keep the team’s work cutting-edge

Skills and experience:

• 8+ years of experience in data engineering and/or cloud/web engineering
• 5+ years of experience in leading the team and being able to directly communicate with the client

Data Engineering:

• Proficiency with cloud ETL tools such as AWS Glue and Azure Data Factory (ADF) for building data pipelines and orchestrating workflows
• Hands-on experience with DBT for data transformation and modeling
• Solid understanding of data modeling techniques and best practices (dimensional, star schema, snowflake, etc.)
• Expertise in SQl for querying and manipulating large datasets
• Experience building, maintaining and optimizing data pipelines and overall data platform
• Experience with data orchestration frameworks and tools such as AWS Lambda, Cloud Functions, and schedulers such as Airflow
• Knowledge of at least one popular programming language used for statistical analysis, such as Python and R
• Experience working with Data Lakes, Data Warehouses and Data Mesh
• Experience with cloud-based infrastructure providers such as Amazon AWS, Microsoft Azure, or Google Cloud Platform

AI/ML:

• Expertise in Large Language Models (LLMs), Natural Language Processing (NLP), and deep learning techniques
• Strong programming skills in Python, TensorFlow, PyTorch, Scikit, Pandas and other AI/ML and data science frameworks
• Deep understanding of machine learning algorithms, data structures, and data processing techniques
• Hands-on experience deploying AI/ML models into production environments

Additional skills:

• Knowledge of reporting and data visualization best practices
• Familiarity with business intelligence tools such as PowerBI, Redash, Tableau, Periscope, and Mode Analytics
• A Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field