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- Design, develop, and maintain AI-powered enterprise applications using React, TypeScript, JavaScript, Python, and Microservices Architecture.
- Leverage GitHub Copilot, Claude Code, Amazon Q Developer, and other AI coding assistants to convert business requirements and user stories into production-ready code and pull requests.
- Architect and develop AI-enabled solutions using LLMs, GenAI services, and Agentic AI frameworks.
- Build scalable backend APIs and microservices integrating AI services with enterprise systems.
- Design and implement Retrieval Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
- Develop conversational AI applications, copilots, virtual assistants, and intelligent workflow automation solutions.
- Implement Prompt Engineering and Context Engineering techniques to improve AI output quality, consistency, and performance.
- Build autonomous and semi-autonomous AI agents capable of orchestrating multi-step business processes.
- Design memory management strategies, including:
- Short-term Memory
- Long-term Memory
- Context Windows
- Token Optimization
- Integrate AI services using:
- Amazon Bedrock
- Claude
- OpenAI
- Other Foundation Model Platforms
- Develop event-driven architectures leveraging Kafka and cloud-native messaging services.
- Collaborate with Product Owners, Architects, Business Analysts, and Development teams to identify AI automation opportunities.
- Implement AI governance, observability, monitoring, and performance optimization practices.
- Participate in Agile development, code reviews, CI/CD automation, and DevSecOps processes.
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