Looking for Senior AI DevOps or Cloud Data Engineer focused on Generative AI (GenAI) observability and cost optimization within the AWS ecosystem.

Bedrock experience a must.

If you're interested in exploring this opportunity, please feel free to share your updated resume at [HIDDEN TEXT]

Additionally, if you know someone in your network who might be a valuable fit and is currently open to new opportunities, I would greatly appreciate your referral.

Looking forward to hearing from you!

Job Title:Senior AI Cloud Engineer (AWS & Generative AI)

Location : Pune, India (Onsite requirement: 4 days/week)

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.

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