AI & Machine Learning Engineer
On-siteSt. Petersburg, Florida, United States
Job Summary
Design, build, and maintain robust data pipelines to collect, clean, and transform data for AI and ML projects. Develop and implement machine learning models and algorithms across the full life cycle, including problem framing, feature engineering, training, evaluation, and deployment. Design and build AI agents that execute within enterprise systems, implementing end-to-end orchestration and evaluation frameworks to ensure accuracy and safety. Create Retrieval-Augmented Generation applications by integrating enterprise knowledge sources with embeddings and vector search. Analyze large datasets to uncover trends, patterns, and insights, and communicate findings through visualizations and reports. Monitor model performance, document processes for transparency, and provide training on data tools and best practices. Consult with IT teams to ensure infrastructure supports stable, highly available applications.
Required Qualifications
- Bachelor's degree in Computer Science, Analytics, or related field from an accredited college or university
- Five (5) or more years of experience in data engineering, data science, or a related role
- hands-on experience in building and deploying machine learning models
- Advanced proficiency in Python and common ML/data libraries such as scikit-learn, TensorFlow, Keras, PyTorch, Pandas, and NumPy
- Strong working knowledge of machine learning methodologies, including supervised learning (e.g., regression, classification) and unsupervised learning (e.g., clustering, dimensionality reduction, anomaly detection)
- Strong SQL skills with experience designing and querying relational databases and supporting data warehousing solutions
- familiarity with ETL/ELT workflows and tools (e.g., SSIS or equivalent)
- Working knowledge of medallion architectures
- Skilled in cloud-based ML development and deployment on platforms such as AWS, Azure, or Google Cloud
- Proficiency with version control and collaborative development workflows, including Git, branching strategies, code review, and basic CI/CD concepts
- Expertise in probability and statistics, including experimental design and hypothesis testing, modeling uncertainty, performance measurement, and selecting appropriate evaluation metrics
- Experience building AI model-powered applications and workflows using model APIs, including prompt design, tool/function calling, structured outputs (JSON), and response validation/guardrails
- Strong understanding of RAG architectures, including document ingestion pipelines, chunking strategies, metadata design, embedding generation, and retrieval methods
- Hands-on experience with vector databases/search systems and tuning retrieval for relevance, latency, and cost
- Must be able to safely operate assigned vehicles possibly long distances
Desired Qualifications
- Masters of Science degree preferred
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