AI & Machine Learning Engineer

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