Responsibilities

• Design, build, and maintain cloud automation workflows using Infrastructure-as-Code tools such as Terraform or CloudFormation

• Develop scalable frameworks for managing infrastructure provisioning, deployment, and configuration across multiple cloud platforms

• Create and integrate service catalog components with automation platforms like Backstage

• Leverage generative AI models to enhance service catalog capabilities, including automated code generation and validation

• Architect and implement CI/CD pipelines for automated build, test, and deployment processes

• Build and maintain deployment automation scripts using technologies such as Python or Bash

• Design and implement generative AI models (e.g., RAG, agent-based workflows) for AIOps use cases like anomaly detection and root cause analysis

• Utilize AI/ML tools such as LangChain, Bedrock, Vertex AI, or Azure AI for building advanced generative AI solutions

• Develop vector databases and document sources using services like Amazon Kendra, OpenSearch, or custom solutions

• Engineer data pipelines for streaming real-time operational insights to support AI-driven automation

• Create MLOps pipelines to deploy and monitor generative AI models, ensuring optimal performance and avoiding model decay

• Evaluate and select appropriate LLM models for specific AIOps use cases, integrating them efficiently into workflows

• Collaborate with cross-functional teams to design and improve automation and AI-driven processes

• Continuously research emerging tools and technologies to improve operational efficiency and scalability

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or related field

• 3-8 years of experience in cloud infrastructure automation, DevOps, and scripting

• Proficiency with Infrastructure-as-Code tools such as Terraform or CloudFormation

• Expertise in Python and generative AI frameworks like RAG and agent-based workflows

• Knowledge of cloud-based AI services, including Bedrock, Vertex AI, or Azure AI

• Familiarity with vector databases like Amazon Kendra, OpenSearch, or custom database solutions

• Competency in data engineering tasks such as feature engineering, labeling, and real-time data streaming

  • Proven experience in creating and maintaining MLOps pipelines for AI/ML models in production environments