AI Engineer III

Quick Facts

• Role: AI Engineer III (hybrid)
• Education: Bachelor’s degree required (Master’s preferred)
• Experience: 3–5+ years shipping ML models to production

Description

Design, develop, deploy, and maintain production-quality AI and machine learning solutions for intelligent automation, predictive insight, and advanced analytics across the enterprise.

Build scalable AI systems (including AI agents and data pipelines), integrate models into enterprise applications, and ensure operational readiness via observability, SLOs, and incident/rollback strategies.

Work with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to transition prototypes into governed, reliable, business-ready systems.

Responsibilities

• Develop and implement ML/AI models with clean, efficient, well-documented production code
• Build data engineering and preprocessing workflows for model inputs
• Design scalable ML pipelines for training, validation, inference, and deployment
• Package and deploy models using established MLOps practices; collaborate with ML Ops
• Monitor deployed models for performance, data drift, and reliability; troubleshoot and resolve issues
• Define and implement SLOs (e.g., p50/p95 latency and availability) and monitoring/alerting for drift, latency, and errors
• Integrate AI capabilities into enterprise workflows, applications, and digital platforms
• Support explainability and documentation for business stakeholders
• Ensure governance: fairness, security, auditability, explainability, traceability, and regulatory compliance
• Evaluate emerging AI technologies (including LLMs/generative AI) to drive innovation
• Implement memory management, context engineering, planning, and multi-step reasoning strategies
• Track quality metrics such as groundedness, faithfulness, relevance, task completion rate, and user satisfaction

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred)
• 3–5+ years of experience designing and shipping ML models to production; led the design of a significant ML-powered feature
• Strong coding skills in Python, Java, or C++ with API development and software design
• Deep understanding of core ML concepts (classification, regression, clustering, deep learning architectures)

Benefits

• Comprehensive benefits (details provided by employer)