Platform Engineer (DevOps & AI/ML/Gen-AI)

Platform Engineer

The role focuses on integrating generative AI into automation workflows, enhancing operational efficiency, and supporting cloud-first initiatives.

Responsibilities

• Design cloud automation workflows using Infrastructure-as-Code tools such as Terraform or CloudFormation
• Build scalable frameworks to manage infrastructure provisioning, deployment, and configuration across multiple cloud platforms
• Create service catalog components compatible with automation platforms like Backstage
• Integrate generative AI models to improve service catalog functionalities, including automated code generation and validation
• Architect CI/CD pipelines for automated build, test, and deployment processes
• Maintain deployment automation scripts utilizing technologies such as Python or Bash
• Implement generative AI models (e.g., RAG, agent-based workflows) for AIOps use cases like anomaly detection and root cause analysis
• Employ AI/ML tools such as LangChain, Bedrock, Vertex AI, or Azure AI for advanced generative AI solutions
• Develop vector databases and document sources using services like Amazon Kendra, OpenSearch, or custom solutions
• Engineer data pipelines to stream real-time operational insights that support AI-driven automation
• Build MLOps pipelines to deploy and monitor generative AI models, ensuring optimal performance and avoiding model decay
• Select appropriate LLM models for specific AIOps use cases and integrate them effectively into workflows
• Collaborate with cross-functional teams to design and refine automation and AI-driven processes
• Research emerging tools and technologies to enhance 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 track record in creating and maintaining MLOps pipelines for AI/ML models in production environments

Nice to have

• Background in Flow Engineering tools such as Langraph or platform-specific workflow orchestration tools
• Understanding of comprehensive AIOps processes to refine cloud-based automation solutions