Sev1Tech LLC is seeking an Artificial Intelligence Integration Engineer to ensure the seamless deployment, monitoring, and optimization of AI models in production, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI-related logging.
Requirements
- Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML).
- Hands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring.
- Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus.
- Expertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins).
- Knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry).
- Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn).
- Understanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI).
Responsibilities
- Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS SageMaker, ensuring scalability and low latency.
- Build and maintain dashboards using Grafana, Prometheus, or Kibana to track real-time model health (e.g., accuracy, latency) and historical trends.
- Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.
- Set up centralized logging with ELK Stack or OpenTelemetry to capture AI inference events, errors, and audit trails for debugging and compliance.
- Develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates, testing, and deployment.
- Apply secure-by-design principles to protect data pipelines and models, using encryption, access controls, and compliance with regulations like GDPR or NIST AI RMF.
- Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource usage on cloud platforms like AWS, Azure, or Google Cloud.
Other
- 5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems.
- Strong problem-solving and debugging skills for resolving pipeline and monitoring issues.
- Excellent collaboration and communication skills to work with cross-functional teams.
- Attention to detail for ensuring accurate and secure dashboard reporting.
- Experience with LLM monitoring tools like LangSmith or Helicone for generative AI applications.
