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
• Implement logging, monitoring, and security controls
• Ensure production readiness and operational excellence

Collaboration & Leadership

• Collaborate with business stakeholders, architects, and AI teams
• Mentor junior engineers and guide technical design decisions

Technical Requirements

• Strong fundamentals in Python
• Experience building REST APIs

• Familiarity with:
• FastAPI / Flask / Django
• JSON, async programming basics

• Basic understanding of:

• LLM APIs (Azure OpenAI or equivalent)
• Prompt-based integrations
• Prompt Engineering

• Exposure to:

• Git and CI/CD pipelines
• Azure cloud fundamentals

• Basic database knowledge (SQL / NoSQL)

Core Engineering

• Advanced proficiency in Python
• Strong experience in:
• FastAPI / Django
• Async programming
• Event-driven architectures
• Microservices design
• Experience with:
• Azure/AWS cloud services
• Containerization (Docker, Kubernetes)
• API management / gateway design

AI & Agentic Capabilities

• Strong understanding of:
• LLM ecosystems (Claude, GPT, Gemini)
• LLM integration patterns
• Prompt engineering (few-shot, structured prompting, chaining)
• Tool invocation frameworks
• Experience with:
• Agentic frameworks and orchestration
• Workflow coordination across multiple AI services
• RAG architectures and patterns
• Vector databases
• Familiarity with:
• MCP connectors or contextual integration frameworks

Enterprise Integration

• Experience integrating AI layers with legacy enterprise systems
• Strong understanding of:
• API scalability
• Distributed system resilience
• High-availability architectures

Preferred Qualifications

• Exposure to GenAI projects
• Basic understanding of multi-step AI workflows
• Familiarity with containerization (Docker – basic level)
• Strong communication skills
• Optional: Exposure to insurance domain (Claims / UW) is a plus

• Proven experience implementing agentic AI systems in production
• Strong prompt engineering expertise
• Exposure to Responsible AI frameworks and governance
• Experience working with distributed/global engineering teams
• Strong stakeholder communication skills
• Domain experience: Insurance – Claims / Underwriting systems is a plus