AI / Machine Learning Engineer

Requirements

• 3+ years of experience collaborating with engineering leads and data science teams on the design, development, and scaling of machine learning systems, with a focus on reliability, scalability, and security.
• 4+ years of hands‑on experience working with public cloud platforms such as Google Cloud Platform, AWS, and/or Azure to deploy, operate, and scale ML workloads.
• 2+ years of hands‑on experience building Infrastructure as Code (IaC) using tools such as Terraform to support ML environments and pipelines.
• 5+ years of software development experience using programming languages such as Python, Java, or equivalent object‑oriented or scripting languages, including production‑grade ML or data applications.
• 3+ years of experience working with machine learning and AI frameworks or platforms such as TensorFlow, scikit‑learn, Anaconda, SageMaker, Vertex AI, or Agentic AI tooling.
• 3+ years of experience understanding model drift and data drift concepts and contributing to the design and implementation of monitoring, validation, and retraining strategies for ML and AI systems.
• 3+ years of working knowledge of CI/CD and MLOps practices, including automated testing, model validation, automated deployments, and integration pipelines.
• 3+ years of experience working in agile development environments, with SAFe experience required.
• 4+ years of experience collaborating and partnering with business leaders, engineering teams, and data science stakeholders to deliver machine learning solutions aligned to business outcomes.

Responsibilities

• Collaborate with Data Scientists to onboard models into monitoring systems (e.MHM database) and maintain configuration files for drift and performance checks.
• Implement monitoring frameworks to track data drift, training-serving skew, and model performance metrics in production environments.
• Configure automated alerts for anomalies in input/output distributions and performance degradation.
• Perform QC checks on incoming data to validate integrity and completeness before model scoring.
• Develop pipelines for continuous validation of data sources and ensure compliance with quality standards.
• Conduct root-cause analysis for performance degradation and recommend retraining strategies.
• Build and maintain CI/CD pipelines for deploying monitoring solutions and model updates.
• Leverage cloud technologies for scalable monitoring and orchestration.
• Maintain detailed logs of monitoring activities, thresholds, and alerts.
• Provide periodic reports on model health, including drift metrics and performance trends.

Benefits

Skills