Required Skills

pythonaws

Job Description

*Senior AI Cloud Engineer**

*Years of Experience: 5 \- 10years**

*Role Overview**

We are seeking a highly technical Cloud Engineer to build and optimize our Generative AI infrastructure. This role focuses on deploying **AWS Bedrock** agents, automating workflows with **Python**, and creating robust observability and billing pipelines for **LLM** usage. You will be responsible for ensuring that our AI services are not only functional but also cost\-effective and highly monitored through advanced logging and alerting.

*Key Responsibilities**

**AI Agent Orchestration:** Design and deploy specialized agents using **AWS Agents for Amazon Bedrock** to automate complex multi\-step business processes.
**Observability \& Alerting:** Build end\-to\-end "Data Log" pipelines. Identify and implement the correct **AWS services for alerting** (e.g., CloudWatch, SNS, or Lambda) based on log anomalies.
**Integration \& Middleware:** Manage and analyze **Mulesoft logs** to ensure seamless connectivity between legacy systems and modern AI services.
**Financial Operations (FinOps):** Monitor **billing metrics** for LLM usage and AWS Bedrock services to prevent cost overruns and optimize token consumption.
**Python Automation:** Write production\-grade Python scripts for data processing, agent logic, and infrastructure automation.

*Technical Requirements (The "Must\-Haves")**

**Core AWS AI Services:** Hands\-on experience with **AWS Bedrock** and **AWS Agent Core** logic.
**Programming:** High proficiency in **Python** (specifically for data manipulation and API integrations).
**Logging \& Monitoring:** Deep understanding of log aggregation. Experience with **Mulesoft** logs is a significant plus.
**Alerting Frameworks:** Ability to determine which AWS service to use for specific alerts (CloudWatch Alarms vs. EventBridge vs. Managed Grafana).
**LLM Knowledge:** Understanding of how LLMs work, including tokenization, prompt engineering, and the cost structure of different models.