• Develop and maintain scalable back-end services using FastAPI and other modern frameworks. • Ensure seamless integration with front-end applications and external services through well-designed RESTful APIs. • Maintenance of integration modules in Node.js/React that connect the frontend to Python APIs. • Contribute to Node.js backend components when required, integrating them with Python services. • Leverage cloud infrastructure (Azure Functions, Azure Storage) for hosting and scaling applications. • Implement security best practices for API authentication (OAuth, JWT) and data protection. • Work on GenAI-driven applications, utilizing frameworks such as LangChain, LlamaIndex, vector databases, and agentic frameworks. • Containerize applications using Docker for environment management.

• Design and build LLM-powered features and AI agent pipelines — from prompt to production • Extend and optimize our RAG / knowledge base system: chunking strategies, embedding models, vector search, hybrid retrieval (BM25 + semantic), and reranking • Implement agentic workflows with tool use, multi-step reasoning and structured outputs • Build document- and email-processing pipelines that leverage LLMs for classification, extraction and summarization • Integrate and manage LLM providers (OpenAI, Anthropic, etc.) and AI SDKs across the stack • Design evaluation frameworks and feedback loops to measure and continuously improve AI output quality • Build and optimize agents for our growing template library • Operate in an AI-first development setup powered by Claude/Cursor

• Lead AI initiatives across the organization • Own and drive implementation of AI-powered solutions • Ensure AI capabilities are integrated into existing systems and practices • Mentor developers and uplift engineering team skills in AI • Research and stay updated with AI technologies and recommend improvements • Establish AI development standards and ensure compliance with best practices • Collaborate with teams to align AI initiatives with business priorities

• Design and build internal AI tooling that supports automated remediation and migration workflows across the developer platform • Deploy and integrate AI tools (including LLM-based assistants, code analysis agents, and CI/CD-integrated tooling) into existing developer workflows • Troubleshoot and support developers experiencing issues with AI tooling — acting as the subject matter expert for AI-assisted development • Collaborate with teams to embed AI capabilities into build practices, upgrade cycles and security patching pipelines • Measure and report on outcomes: faster upgrade cycles, reduced manual remediation effort, and cost avoidance • Continuously improve tooling based on developer feedback and usage patterns