Senior AI Cloud Engineer – AWS & Generative AI

Zorba AI; Maharashtra; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Zorba AI is seeking a Senior AI Cloud Engineer to design, deploy, monitor, and optimize AI infrastructure on AWS Bedrock. You will build and operate scalable GenAI workloads and agent orchestration with a focus on reliability and cost control.

You will implement comprehensive logging and observability pipelines using CloudWatch, Lambda, and EventBridge, and collaborate with engineering teams to deliver production-grade GenAI solutions across cloud services.

Qualifications

• Proficient with AWS Bedrock and Bedrock Agents for building scalable GenAI workloads.
• Strong Python programming and automation capabilities.
• Experience with Generative AI/LLMs and cost optimization strategies.
• Extensive knowledge of AWS services and observability tooling.
• Familiarity with logging, monitoring, and alerting practices for production systems.
• Understanding of LLM tokenization, pricing, and model usage considerations.
• Experience with API integrations and data processing pipelines.
• Proven ability to troubleshoot production issues in cloud environments.

Responsibilities

• Design and deploy AI agents using AWS Bedrock for complex workflows.
• Develop and maintain Python-based automation for data processing and cloud operations.
• Build end-to-end logging, monitoring, and observability pipelines for AI/LLM workloads.
• Analyze logs from applications and MuleSoft and integrate with AWS monitoring solutions.
• Implement alerts using CloudWatch, SNS, Lambda, and EventBridge.
• Monitor Bedrock usage, token consumption, and cost metrics to optimize spend.
• Collaborate with teams to deliver reliable GenAI solutions and maintain performance.
• Tackle production issues across APIs, logs, integrations, and AWS services.

Skills

AWS Bedrock

Bedrock Agents

Python

Generative AI/LLMs

AWS Cloud services

CloudWatch/Lambda/SNS/EventBridge

Logging/Observability

LLM tokenization/prompt engineering

API integrations

Troubleshooting/production support

Tools

MuleSoft

Amazon Managed Grafana

AWS Cost Explorer

Bedrock Knowledge Bases

Job description

We are looking for a highly skilled Senior AI Cloud Engineer with strong expertise in AWS, Amazon Bedrock, Python, and Generative AI. The role will focus on designing, deploying, monitoring, and optimizing AI infrastructure and AWS Bedrock Agents. The ideal candidate should have strong experience in AI agent orchestration, observability, automation, LLM cost monitoring, and AWS cloud services.

Key Responsibilities

• Design and deploy AI agents using AWS Agents for Amazon Bedrock for complex business workflows.
• Develop and maintain Python-based automation for data processing, API integrations, agent workflows, and cloud operations.
• Build end-to-end logging, monitoring, and observability pipelines for AI/LLM workloads.
• Analyze application and MuleSoft logs and integrate them with AWS monitoring and alerting solutions.
• Implement alerts using Amazon CloudWatch, SNS, Lambda, EventBridge, and other AWS services based on operational requirements.
• Monitor AWS Bedrock and LLM usage, billing, token consumption, and cost metrics.
• Identify opportunities to optimize LLM usage and reduce unnecessary cloud/AI costs.
• Work with different LLM models and understand their tokenization, pricing, context limits, and performance characteristics.
• Develop production-grade solutions for Generative AI and cloud infrastructure.
• Troubleshoot production issues across AI agents, APIs, logs, integrations, and AWS services.
• Collaborate with engineering and business teams to deliver reliable and scalable GenAI solutions.

Must-Have Skills

• AWS Bedrock - Mandatory
• Hands-on experience with AWS Bedrock Agents / Agent Core concepts
• Strong Python programming and automation skills
• Experience with Generative AI / LLMs
• Strong knowledge of AWS Cloud services
• Experience with CloudWatch, Lambda, SNS, EventBridge and alerting mechanisms
• Strong understanding of logging, monitoring, and observability
• Knowledge of LLM tokenization, prompt engineering, model usage, and cost optimization
• Experience with API integrations and data processing
• Strong troubleshooting and production support experience

Good-to-Have Skills

• MuleSoft / MuleSoft log analysis
• Amazon Managed Grafana
• AWS Cost Explorer / AWS Billing and FinOps
• Experience building AI/LLM usage dashboards
• Experience with Bedrock Knowledge Bases and RAG
• Experience with REST APIs and middleware integrations
• Infrastructure automation / IaC

Skills: aws,automation,python,bedrock