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. 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.