Cloud Data Engineer with AI

• 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.

Must-Have Skills
• 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.
• Terraform: Experience with Terraform for Infrastructure as Code and cloud resource management.
• Cloud-Native Development: Understanding of scalable application architecture, cloud services, and distributed systems.
• Cloud Security: Knowledge of authentication, authorization, encryption, and access management.
• Software Engineering: Understanding of coding standards, testing, debugging, version control, and application development best practices.
Good-to-Have Skills
• Experience with CI/CD tools such as Jenkins, GitHub Actions, and GitLab CI.
• Knowledge of Docker and container technologies.
• Familiarity with AI productivity tools such as Cursor and GitHub Copilot.
• Experience with Agile/Scrum methodologies and Jira.
• Knowledge of cloud monitoring, logging, and deployment automation.
• Experience with multi-region and multi-currency applications.
• Understanding of security, governance, compliance, and data protection.
• Good communication, problem-solving, and collaboration skills.
• Ability to provide technical guidance and work effectively with cross-functional teams.