Python Developer (AI Integration Focus)
Junior (4-7 years)
Senior (8-12 years)
Role Overview
Support development and integration of Gen AI-enabled services, including LLM integrations and emerging agent-based workflows. Work under senior guidance to build scalable APIs and automation components in a cloud-based enterprise environment.
Design and build scalable, enterprise-grade systems integrating GenAI and agentic orchestration frameworks into core business platforms. Lead the development of multi-agent workflows, real-time integrations, and cloud-native architectures, enabling intelligent automation and AI-driven enterprise applications.
Key Responsibilities
- Develop Python-based APIs and backend services
- Integrate LLM APIs into applications
- Design and refine prompts for LLM-based applications
- Support development of simple AI workflows using leading IDEs (VS Code, Cursor, Pycharm)
- Assist in deployment on Azure
- Support integration with core systems and workflow platforms
- Debug, test, and optimize application components
- Maintain documentation and technical specifications
- Experience in using coding agents (GitHub copilot, Cursor etc)
Architecture and Engineering
- Architect and develop Python-based microservices for GenAI and enterprise platforms
- Design and implement cloud-native and serverless architectures (Azure/AWS)
- Build scalable APIs and backend systems for high-performance enterprise environments
- Deploy, manage, and optimize services in cloud environments
Agentic AI & Orchestration
- Design and implement agentic workflows and orchestration layers
- Build multi-agent systems and AI orchestration services
- Implement Agent-to-Agent (A2A) integration patterns
- Design agent registries and service discovery frameworks
- Enable tool-calling frameworks within LLM-driven workflows
RAG & AI Pipelines
- Design, implement, and manage:
- RAG pipelines and architectures
- Embedding workflows
- Real-time document processing pipelines
- Optimize retrieval accuracy and pipeline performance
Enterprise Integration
- Integrate AI systems with enterprise platforms using:
- MCP connectors (Model Context Protocol or equivalent)
- APIs, middleware, and event-driven integrations
- Ensure high scalability, resilience, and fault tolerance
Performance & Operations
- Optimize message handling and high-volume system interactions
- Implem