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.

Responsibilities:

The list of essential functions, as outlined herein, is intended to be representative of the duties and responsibilities performed within this classification. It is not necessarily descriptive of any one position in the class. The omission of an essential function does not preclude management from assigning duties not listed herein if such functions are a logical assignment to the position.

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.

Qualifications:

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, 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.

PHYSICAL REQUIREMENTS:

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.

Functions involve the periodic performance of moderately physically demanding work, usually involving lifting, carrying, pushing and/or pulling of moderately heavy objects and materials (up to 25 pounds). Tasks that require moving objects of significant weight require the assistance of another person and/or use of proper techniques and

moving equipment. Tasks may involve some climbing, stooping, kneeling, crouching, or crawling. Must be able to safely operate assigned vehicles possibly long distances.

ENVIRONMENTAL REQUIREMENTS:

The work environment characteristics described here are representative of those an employee may encounter while performing the essential functions of this job.

Functions are regularly performed inside and/or outside with potential for exposure to adverse conditions, such as inclement weather, atmospheric elements and pathogenic substances. The noise level in the work environment is usually moderate.