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1d ago

AI & Machine Learning Engineer

OneBlood Saint Petersburg, FL

Saint Petersburg, FL

1d ago

Full time Agentic Ai Systems Ai Ml Artificial Intelligence

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Job Description

Oversee the end-to-end delivery of AI and Machine Learning solutions, from data pipelines and model development to agent and RAG application implementation.

Responsibilities

Design, build, and maintain robust data pipelines to collect, clean, and transform data from multiple sources for analysis, modeling, and deployed operational environments
Develop and implement ML models and algorithms across the full life cycle, including problem framing, data collection, data preparation, feature engineering, model selection, training, evaluation, deployment, retraining, and ongoing advancement
Design and build AI agents that run in enterprise workflows within systems such as databases, CRMs, ticketing, and knowledge bases, with reliable and safety guardrails
Implement end-to-end agent orchestration, including prompting, memory/state, tool-calling, and retries or fallbacks
Create evaluation frameworks for agents (test suites, simulations, and human-in-the-loop review) to improve accuracy and reduce error
Design, build, and maintain Retrieval-Augmented Generation (RAG) GPT applications by integrating enterprise knowledge sources (documents and databases) with embeddings, vector search, and prompt orchestration
Ensure RAG applications deliver accurate, grounded responses with evaluation and safety guardrails
Analyze large datasets to identify trends, patterns, and insights; produce visualizations and reports for stakeholders
Monitor and evaluate performance of data models and systems, and make adjustments to optimize accuracy and efficiency
Document processes, methodologies, and model development for transparency and reproducibility
Provide training and support to other team members or departments on data tools, techniques, and best practices
Consult with internal IT teams to ensure infrastructure supports stable, well-designed, highly available, and well-maintained Data Science and AI applications
Stay current with emerging technologies and industry trends to improve data engineering practices and support development of cutting-edge solutions
Ensure data accuracy, consistency, and security; implement and enforce data governance policies and best practices

Requirements

Education: Bachelor’s degree in Computer Science, Analytics, or a related field (Master of Science preferred)
Experience: 5+ years of experience in data engineering, data science, or a related role, including hands-on experience building and deploying machine learning models
Advanced proficiency in Python and common ML/data libraries including scikit-learn, TensorFlow, Keras, PyTorch, Pandas, and NumPy for building, training, and evaluating models
Strong knowledge of ML methodologies, including supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction, anomaly detection)
Strong SQL skills, including 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
Experience with cloud-based ML development and deployment on 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

Technologies

Artificial Intelligence (AI), Machine Learning (ML)
Python, scikit-learn, TensorFlow, Keras, PyTorch
Pandas, NumPy
SQL, ETL, ELT, SSIS
AWS, Azure, Google Cloud
Git, CI/CD, JSON
RAG, vector databases/search systems
Embeddings, vector search
Model APIs

Physical Requirements

Periodic moderately physically demanding work involving lifting, carrying, pushing, and/or pulling moderately heavy objects and materials (up to 25 pounds)
Tasks requiring moving objects of significant weight require assistance from another person and/or proper techniques and moving equipment
May involve some climbing, stooping, kneeling, crouching, or crawling
Must safely operate assigned vehicles, possibly long distances

Environmental Requirements

Work performed regularly inside and/or outside with potential exposure to adverse conditions, including inclement weather, atmospheric elements, and pathogenic substances
Noise level is usually moderate

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