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Role Overview
We are looking for a highly experienced Senior AI Cloud Engineer with 12+ years of experience in Cloud Engineering, DevOps, Data Engineering, or AI Infrastructure, with strong hands-on expertise in AWS and Generative AI.
The role will focus on building and optimizing GenAI infrastructure, AWS Bedrock agents, observability pipelines, LLM usage monitoring, and cloud cost optimization. The ideal candidate should be hands-on with Amazon Bedrock, Python, AWS monitoring services, LLM observability, and FinOps.
AWS Bedrock Experience Is Mandatory.
Key Responsibilities
Design, develop, and deploy AI agents using AWS Agents for Amazon Bedrock.
Build scalable GenAI infrastructure and supporting cloud services on AWS.
Develop end-to-end observability and data-log pipelines for GenAI workloads.
Implement monitoring and alerting using CloudWatch, EventBridge, SNS, Lambda, and related AWS services.
Analyze application and MuleSoft logs to identify failures, anomalies, and integration issues.
Monitor Amazon Bedrock usage, token consumption, model utilization, and billing metrics.
Implement GenAI cost optimization and FinOps strategies to control LLM/AWS expenditure.
Develop production-grade Python scripts and automation for data processing, API integration, AI workflows, and cloud operations.
Design dashboards and reporting mechanisms for LLM performance, usage, cost, and operational health.
Evaluate and select appropriate AWS services for different monitoring, alerting, and automation requirements.
Troubleshoot production issues across AI, cloud, integration, and observability layers.
Work with architecture, DevOps, data, and application teams to establish scalable and reliable GenAI solutions.
Mandatory Skills
12+ years of overall experience in Cloud Engineering, DevOps, Data Engineering, AI Engineering, or related technologies.
Strong hands-on experience with AWS.
Mandatory hands-on experience with Amazon Bedrock.
Experience with AWS Agents for Amazon Bedrock / Agent-based GenAI solutions.
Strong Python programming experience.
Experience with LLM observability and monitoring.
Strong knowledge of:
AWS CloudWatch
AWS Lambda
Amazon SNS
Amazon EventBridge
AWS logging and monitoring
Understanding of LLM tokenization, prompt engineering, model usage, and pricing/cost structures.
Experience monitoring and optimizing GenAI/AWS costs.
Skills: cloud,aws,devops,python,bedrock
Work arrangement
No
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