Overview
Remote
Depends on Experience
Contract - W2
Contract - 6 Month(s)
Skills
AI Engineer
AI
Dynatrace
MLflow
EvidentlyAI
Job Details
AI Sustaining Engineer Contract Remote
Project summary
The AI Sustainability Engineer ensures that AI models deployed in production remain accurate, efficient, ethical, and cost-effective over time. This role bridges MLOps, observability, and optimization, focusing on performance monitoring, drift detection, retraining workflows, and sustainable resource use.
Key Responsibilities:
- Monitor AI model performance, reliability, and fairness in production.
- Detect and remediate data drift, bias, and degradation issues.
- Optimize model inference efficiency, scalability, and energy usage.
- Implement observability frameworks and automated retraining triggers.
- Collaborate with AI Engineers and infrastructure teams to sustain production health.
- Provide technical support and troubleshooting for live AI systems.
- Report AI policy violations and ensure compliance.
Must have Skills
- Experience with ML observability tools (Dynatrace, MLflow, EvidentlyAI, Prometheus, Grafana, etc.).
- Strong understanding of data drift detection and statistical monitoring.
- Hands-on with containerization (Docker/Kubernetes) and CI/CD pipelines.
- Experience in production support and AI monitoring.
- Proficient in Python for automation and monitoring scripts.
- Familiarity with model versioning, governance, and retraining pipelines.
- Knowledge of AWS cloud AI infrastructure (containerized deployments on EC2 instances).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.