Senior AI Cloud Engineer (AWS & Generative AI)

Job Description

**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.