Python Developer - Strategic Hiring 2027

Genzeon Global; Hyderabad; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Genzeon Global in Hyderabad is seeking a Senior Python Developer to design, develop, and deliver scalable cloud-native microservices and AI-powered back-end solutions using Python (FastAPI, Async I/O). You will build robust data pipelines and mentor junior engineers.

You will work with Confluent Kafka for high-throughput messaging, implement CI/CD with GitHub Actions, containerize with Docker, and manage infrastructure via Terraform on GCP.

Qualifications

• Expert-level Python with FastAPI and Async I/O.
• Experience with Confluent Kafka for high-throughput messaging.
• Strong testing using Pytest or unittest.

Responsibilities

• Design cloud-native microservices and Async backends.
• Build scalable APIs and real-time event-driven systems with Kafka.
• Integrate AI capabilities including LLMs and RAG workflows.
• Implement CI/CD pipelines using GitHub Actions and IaC with Terraform.
• Mentor junior engineers and collaborate with product stakeholders.

Skills

Python

FastAPI

Async IO

Kafka

Testing

CI/CD

Terraform

Docker

Observability

Mentoring

Tools

GitHub Actions

Terraform

Docker

Job description

Python Developer - Strategic Hiring 2027

We are seeking a highly experienced Senior Python Developer to design, develop, and deliver scalable cloud-native microservices, event-driven architectures, and modern AI/LLM-powered solutions. In this role, you will build robust back-end systems using Python (FastAPI, Async I/O), develop data and messaging pipelines, integrate Enterprise AI capabilities (LLMs, RAG, Agentic workflows), uphold engineering best practices, and mentor junior engineers.

Key Responsibilities

Technical Design & Development

• Design and implement cloud-native microservices and Async backend architectures using Python (FastAPI, Pydantic, Async I/O).
  
• Build and optimize scalable APIs, background worker services, and real-time event-driven systems using Confluent Kafka.
  
• Drive performance tuning, distributed caching with Redis, and structured data transformation pipelines (including JSON, Avro, XSLT, and XML/XSD processing).
  
• Follow and contribute to engineering standards, conduct code reviews, and apply comprehensive testing strategies (Pytest / unittest).
  

AI & Generative Engineering Solutions

• Design and integrate Enterprise AI capabilities, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, and agentic workflows, into production backend environments.
  
• Develop deterministic pipelines for context retrieval, enterprise search, structured prompt engineering, and function-calling integrations.
  
• Implement evaluation, logging, and guardrail frameworks for AI/LLM microservices to ensure reliability, low latency, and security.
  

DevOps, Infrastructure & Observability

• Implement secure, scalable CI/CD pipelines (using GitHub Actions) and manage infrastructure via Infrastructure-as-Code (Terraform).
  
• Handle containerization (Docker) and deployments on cloud infrastructure (GCP Cloud Run, Pub/Sub, Secret Manager, Artifact Registry, Eventarc).
  
• Integrate full-stack monitoring and observability systems using New Relic, Cloud Logging, and Cloud Monitoring.
  

Team Collaboration & Stakeholder Management

• Mentor junior engineers on software design, Async Python patterns, and emerging AI frameworks.
  
• Collaborate with product managers, cross-functional teams, and client stakeholders to translate business requirements into robust technical solutions.
  

Mandatory Skills

Programming & Core Frameworks

• Expert-level Python: Advanced proficiency in FastAPI, Pydantic (Pydantic Settings), and Async I/O.
  
• Event-Driven Architecture: Strong hands-on experience with Confluent Kafka for high-throughput messaging.
  
• Testing & Quality: Advanced expertise in unit/integration testing frameworks (Pytest, unittest).
  

AI & LLM Integration

• Hands-on experience integrating LLMs (OpenAI, Anthropic, Gemini, or open-source models via vLLM/Ollama) into production microservices.