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 up-to-date 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.

Skills: python,automation,bedrock,aws

📌 Senior AI Cloud Engineer (AWS & Generative AI) (Pune)
🏢 Zorba AI
📍 Pune