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 analyse 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 how LLMs work, including tokenization, prompt engineering, and the cost structure of different models