ai-engineer

To deploy production-grade AI/ML systems at scale, you provision a model, deploy it behind an API, and enable continuous monitoring and automated evaluation. This requires architecture design, MLOps pipelines, and model serving abstraction.

End-to-end MLOps pipelines for model serving include data processing, model training, evaluation, registry, and deployment. These pipelines ensure scalable production AI/ML workloads are monitored and governed reliably across their lifecycle.

RAG integration with multi-agent workflows in production uses retrieval-augmented workflows and multi-agent orchestration. This architecture design enables scalable AI systems to retrieve context and delegate tasks effectively across agents.

Yes, you can use this for model selection and fine-tuning in production environments. The core task covers selecting a model, fine-tuning it, serving it behind an API, and enabling monitoring and automated evaluation.

Yes, automated evaluation and monitoring are required for scalable AI/ML systems. Production-grade model serving requires continuous tracking and governance to ensure reliability and performance across ML workloads.