AI & Machine Learning Engineer

Overview

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.

• 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.

To perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.

EDUCATION AND/OR

EXPERIENCE:

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.

CERTIFICATES, LICENSES, REGISTRATIONS AND DESIGNATIONS:

None

KNOWLEDGE, SKILLS AND ABILITIES:

• Advanced proficiency in Python and common ML/data libraries such as scikit-learn, Tensor Flow, Keras, PyTorch, Pandas, and Num Py 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…