About the Project - GenAI Assistant

We are building production-grade agentic content workflows for an English Language Learning platform. It uses AI agent architectures to generate and validate educational content at scale, serving multiple products across the organization. The team owns the end-to-end lifecycle: workflow design, LLM orchestration, evaluation, and production operations.

About the Role

We are seeking an experienced Python AI Engineer to join our team and contribute to the development of a next-generation GenAI assistant designed to support and streamline English-language learning content creation. The solution is built using an agentic architecture (LangGraph) enabling autonomous, modular, and collaborative agent behaviours. The assistant’s knowledge base integrates multiple data sources, uploaded files, curated datasets, and relevant web content, ensuring broad coverage, high accuracy, and adaptability across diverse exam formats.

Key Responsibilities:

• Design, develop, and optimize AI-driven features for a GenAI assistant focused on English-language exam tasks. • Build and maintain agentic workflows using LangGraph. • Develop backend services and APIs using Python and FastAPI. • Integrate and manage cloud resources across Azure (Container Apps, Key Vaults, Azure Foundry) and AWS. • Implement monitoring and observability solutions (e.g., New Relic or equivalent). • Collaborate with cross-functional teams to define requirements and deliver high-quality features. • Ensure robust CI/CD pipelines and contribute to overall SDLC best practices. • Package, deploy, and scale applications using Docker; Kubernetes experience is a strong plus.

Required Skills & Qualifications: • 3–5+ years of professional experience in Python development, with strong knowledge of FastAPI. • Hands-on experience with AI/ML frameworks, especially CrewAI, LangChain, LangGraph, or similar agentic/LLM toolkits. • Practical experience with Azure services (Container Apps, Key Vault, Azure Foundry) and familiarity with AWS environments. • Strong understanding of monitoring and observability tools. • Solid grasp of CI/CD processes and modern DevOps practices. • Working knowledge of Docker; Kubernetes experience is a plus. • Understanding of SDLC and agile development methodologies