Cloud AI Engineer

Key Responsibilities

• Design and develop scalable AI/GenAI solutions on AWS and Azure.
• Build and deploy RAG applications, AI agents, LLM-based APIs, and intelligent automation solutions.
• Develop serverless and event-driven AI applications using AWS and Azure native services.
• Integrate LLM applications with AWS Lambda and Azure Functions.
• Design scalable distributed and event-driven cloud architectures.
• Develop production-grade AI services using Python.
• Build REST APIs using FastAPI, Flask, or similar frameworks.
• Implement CI/CD pipelines and Infrastructure-as-Code for cloud workloads.
• Implement monitoring, logging, security, governance, and cost optimization.
• Work with enterprise applications, APIs, databases, and cloud platforms.
• Participate in architecture discussions and provide technical guidance to engineering teams.

Technical Skills

Generative AI & LLMs

• Amazon Bedrock and/or Azure OpenAI.
• Azure AI Foundry and Azure AI Search.
• Amazon SageMaker and/or Azure Machine Learning.
• LLMs, embeddings, vector databases/search, and RAG architecture.
• Prompt engineering, AI agents, and agent frameworks.
• LLM evaluation, observability, guardrails, and responsible AI.
• Serverless RAG and event-driven GenAI workflows.

Model Context Protocol & AI Agents

• Understanding of Model Context Protocol (MCP) and experience building or configuring MCP servers.
• Experience developing AI agents using Bedrock Agents, Agent Core, or similar frameworks.

Python & Application Development

• Strong Python programming skills.
• Experience with Lambda handlers and boto3.
• Experience with LangChain and/or LlamaIndex.
• REST API development using FastAPI, Flask, or equivalent.
• Knowledge of JSON, REST APIs, authentication, OAuth/OIDC, and API integrations.
• Experience with asynchronous programming and distributed systems.

Cloud, Serverless & DevOps

• Strong hands-on experience with AWS and Azure.
• Serverless development using Lambda, API Gateway, S3, DynamoDB, and Azure Functions.
• Docker, ECS, EKS, and/or Kubernetes.
• Infrastructure-as-Code using Terraform, AWS CDK/SAM, or Azure Bicep.
• CI/CD using GitHub Actions, GitLab, Jenkins, Azure DevOps, or equivalent.
• MLOps/LLMOps experience using MLflow, SageMaker, Azure ML, or similar platforms.
• Monitoring and observability using CloudWatch, Azure Monitor, Application Insights, OpenTelemetry, or similar tools.

Additional Requirements

• Minimum 5+ years of hands-on experience in Cloud and GenAI technologies.
• Experience delivering production-grade AI/GenAI applications.
• Strong troubleshooting, system design, and problem-solving skills.
• Experience using AI coding tools such as Amazon Q Developer, GitHub Copilot, Cursor, or Kiro.
• Strong understanding of software engineering principles, design patterns, and clean code.