Required Skills: Machine learning, MLOPS, AI Model, AI monitoring, AI Sustainability, Machine learning observability, Dynatrace, Grafana, python, Prometheus, Data Drift,

Job Description

Job Role:           AI Sustaining Engineer

Location:           Remote

Client:              Apex Systems/ Prime Therapeutics

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Project summary

Prime Therapeutics is building out an all-new AI team to support a multitude of initiatives. They are building out a vast roadmap through 2029 outlook, including use cases involving Prior Authorization, Rebates, Summarization.

 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).
• Strong communication, team player, go-getter type attitude. Must be proactive and seeking out work and solutions, good working in ambiguity and not always being handed instructions.