AI & Machine Learning Engineer

OneBlood • Orlando, FL

1d

About The Position

Oversees the coding, pipeline development, execution, and delivery of Artificial Intelligence (AI) and Machine Learning (ML) projects across the organization. Works with cross-functional teams and leverages advanced analytics, applied statistics, AI and ML techniques to drive business insights and optimize operations.

Requirements

• Bachelor’s degree in Computer Science, Analytics, or related field from an accredited college or university. Masters of Science degree preferred.
• Five (5) or more years of experience in data engineering, data science, or a related role, with 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 for building, training, and evaluating models
• 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.

Responsibilities

• Designs, builds, and maintains robust data pipelines to collect, clean, and transform data from various sources used in analysis, modeling, and deployed operational environments
• Develops and implements ML models and algorithms to solve complex business problems and improve decision-making processes across the full life cycle, including problem framing, data collection, data preparation, feature engineering, model selection, training, evaluation, deployment, retraining, and advancement
• Designs and builds AI agents that execute in workflows within enterprise systems (databases, CRMs, ticketing, knowledge bases) and that are deployed with reliable/safety guardrails
• Implements end-to-end agent orchestration (prompting, memory/state, tool-calling, retries/fallbacks) and develops evaluation frameworks (test suites, simulations, human-in-the-loop review) to improve accuracy and reduce error
• Designs, builds, and maintains Retrieval-Augmented Generation (RAG) GPT applications by integrating enterprise knowledge sources (documents/databases) with embeddings, vector search, and prompt orchestration to deliver accurate, grounded responses with evaluation and safety guardrails
• Analyzes large datasets to uncover trends, patterns, and insights, and creates visualizations and reports to communicate findings to stakeholders
• Monitors and evaluates the performance of data models and systems, and makes necessary adjustments to optimize accuracy and efficiency
• Documents processes, methodologies, and model development to ensure transparency and reproducibility
• Provides training and support to other team members or departments on data tools, techniques, and best practices
• Consults with internal IT teams to ensure infrastructure supports stable, well-designed, highly available, and well-maintained Data Science and AI applications
• Stays current with emerging technologies and industry trends to continuously improve data engineering practices and contributes to the development of cutting-edge solutions
• Ensures the accuracy, consistency, and security of data; implements and enforces data governance policies and best practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level