AI & Machine Learning Engineer

What you’ll do

• Design, build, and maintain robust data pipelines to collect, clean, and transform data from multiple sources for analysis, modeling, and deployed operational workflows.
• Develop and implement machine learning models across the full life cycle, including problem framing, data collection and preparation, feature engineering, model selection, training, evaluation, deployment, retraining, and continued advancement.
• Design and build AI agents that execute within enterprise workflows across systems such as databases, CRMs, ticketing, and knowledge bases, ensuring deployments include reliable and safety guardrails.
• Implement end-to-end agent orchestration, including prompting, memory/state, tool-calling, and retries/fallbacks, along with evaluation frameworks such as test suites, simulations, and human-in-the-loop review to improve accuracy and reduce errors.
• Build and maintain Retrieval-Augmented Generation (RAG) GPT applications by integrating enterprise knowledge sources (documents and databases) using embeddings, vector search, and prompt orchestration, with evaluation and safety guardrails for grounded responses.
• Analyze large datasets to identify trends, patterns, and insights, and create visualizations and reports to communicate findings to stakeholders.
• Monitor and evaluate performance of data models and systems, making adjustments to optimize both accuracy and efficiency.
• Document processes, methodologies, and model development to ensure 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 help 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 continuously improve data engineering practices and contribute to cutting-edge solutions.
• Ensure data accuracy, consistency, and security by implementing and enforcing data governance policies and best practices.

What you bring

• Bachelor’s degree in Computer Science, Analytics, or a related field from an accredited college or university; Master of Science preferred.
• 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 ML/data libraries including scikit-learn, TensorFlow, Keras, PyTorch, Pandas, and NumPy.
• Strong knowledge of machine learning methods, 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; familiarity with ETL/ELT workflows and tools such as SSIS or equivalents.
• Working knowledge of medallion architectures.
• Experience with cloud-based ML development and deployment on AWS, Azure, or Google Cloud.
• Proficiency with version control and collaboration workflows, including Git, branching strategies, code review, and basic CI/CD concepts.
• Expertise in probability and statistics, including experimental design, hypothesis testing, modeling uncertainty, performance measurement, and choosing 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, 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.

Key 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 & environmental requirements

• Work may involve periodic moderately physically demanding tasks, including lifting, carrying, pushing, and pulling of moderately heavy objects and materials up to 25 pounds. Assistance and proper techniques/equipment are required for moving heavier items.
• Tasks may include climbing, stooping, kneeling, crouching, or crawling. Must be able to safely operate assigned vehicles, possibly for long distances.
• Work occurs inside and/or outside with potential exposure to inclement weather, atmospheric elements, and pathogenic substances. Noise level is usually moderate.