Senior AI Cloud Engineer (AWS & Generative AI)

engineeringjobs.net, Inc.; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

engineeringjobs.net, Inc. is seeking a highly technical Cloud Engineer to build and optimize Generative AI infrastructure, focusing on deploying AWS Bedrock agents, automating workflows with Python, and creating robust observability and billing pipelines for LLM usage.

The role emphasizes cost efficiency, advanced logging, alerting, and seamless integration between legacy systems and modern AI services, with hands-on work on monitoring and FinOps practices.

Qualifications

• Experience with AWS Bedrock and AWS Agent Core logic.
• Proficient Python for data manipulation and API integrations.
• Strong logging and monitoring with familiarity of Mulesoft logs.
• Ability to determine AWS alerting services (CloudWatch vs EventBridge vs Grafana).
• Understanding of LLMs, tokenization, and cost considerations.

Responsibilities

• Design and deploy AI agents using AWS Bedrock and related tooling.
• Build end-to-end data log pipelines and alerting using CloudWatch/SNS/Lambda.
• Analyze and monitor Mulesoft logs for connectivity between systems.
• Monitor billing for LLM usage and Bedrock services to optimize cost.
• Develop production-grade Python scripts for automation and data processing.

Skills

Python

AWS Bedrock

LLM knowledge

API integrations

Logging & monitoring

Alerting frameworks

Tools

Mulesoft

CloudWatch

EventBridge

Grafana

AWS Agent Core

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.