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Sr AI Cloud Engineer (AWS & Generative AI)

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Onsite

Sr AI Cloud Engineer (AWS & Generative AI)

Zorba AI

Sr AI Cloud Engineer (AWS & Generative AI)

Skills

PythonAWS LambdaGrafanaAIAWS

22 days ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

5 October 2026

What they need


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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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