Cloud AI Engineer

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• Design, develop, and maintain AI-powered applications using Python, Generative AI, and LLMs.
• Develop and integrate LLM-based solutions for business applications and automation.
• Build and maintain RAG pipelines to retrieve relevant information from enterprise data sources.
• Implement prompt engineering techniques to improve AI response quality and accuracy.
• Work with vector databases and vector stores for embeddings, semantic search, and information retrieval.
• Develop and integrate AI gateways, MCP services, and guardrails for secure AI application development.
• Integrate AI models using AWS Bedrock, Azure AI, or Google Cloud AI services.
• Develop and maintain secure REST APIs using Python, AWS Lambda, and API Gateway.
• Use AWS services such as S3, EC2, IAM, KMS, and container technologies to build and deploy applications.
• Implement Infrastructure as Code using Terraform to automate cloud infrastructure provisioning.
• Follow cloud-native development practices to build scalable, reliable, and secure applications.
• Implement authentication, authorization, encryption, and access control mechanisms.
• Develop and maintain CI/CD pipelines for automated application testing and deployment.
• Work with Docker and container technologies for application deployment.
• Monitor application performance, troubleshoot issues, and optimize cloud resource utilization.
• Collaborate with developers, architects, DevOps teams, and business stakeholders to deliver technical solutions.
• Participate in code reviews, technical discussions, and Agile/Scrum activities.
• Maintain technical documentation and follow software development and cloud security best practices.

Required qualifications to be successful in this role

• Python: Strong programming skills in Python for application development and automation.
• Generative AI & LLMs: Experience developing applications using Generative AI and Large Language Models.
• RAG: Knowledge of Retrieval-Augmented Generation, document processing, embeddings, and information retrieval.
• Vector Databases: Experience with vector stores, semantic search, and similarity search.
• AI Gateway, MCP & Guardrails: Understanding of AI gateways, Model Context Protocol, and AI security guardrails.
• AWS Cloud Services: Hands-on experience with AWS Lambda, S3, API Gateway, EC2, IAM, KMS, and containers.
• Cloud AI Platforms: Experience with AWS Bedrock, Azure AI, or Google Cloud AI.
• Prompt Engineering: Ability to create and optimize prompts for LLM-based applications.
• API Development: Experience developing and integrating RESTful APIs.