Lead AI and Python Developer

Role Overview

Develop AI Next platform services and product features by building GenAI, RAG, Agentic AI, and AI Assistant capabilities. Design and develop APIs and microservices, integrate LLMs, vector databases, and enterprise systems, and implement testing, monitoring, and observability. The role also involves AI model development, building and training models for specific business use cases, and leveraging LLM technologies such as ChatGPT/OpenAI to create GenAI solutions through prompt engineering and related capabilities.

Key Responsibilities

• Develop AI Next platform services and product features.
• Build and enhance GenAI, RAG (Retrieval-Augmented Generation), Agentic AI, and AI Assistant capabilities.
• Design, develop, and maintain REST APIs and microservices.
• Integrate LLMs, vector databases, and enterprise applications/systems.
• Develop AI models and train models for specific business purposes and use cases.
• Identify and implement use cases that can leverage LLM technologies such as ChatGPT/OpenAI.
• Build capabilities around Prompt Engineering and Generative AI.
• Implement testing, monitoring, logging, and observability for AI applications and services.
• Develop scalable and secure backend services using Python frameworks.
• Collaborate with cross-functional teams to deliver AI-powered solutions.
• Deploy and manage applications using containerization and orchestration platforms.

Required Skills

Advanced Python
Distributed Systems
GenAI Architecture
Agentic AI Design Patterns
RAG Optimization
Model Serving
Kubernetes
Cloud Platforms
Responsible AI

Preferred Candidate Profile

• 10+ Years of experience with design and end to end architecture
• Strong experience in Python-based backend development.
• Hands-on experience with GenAI, RAG, Agentic AI, and AI Assistant implementations.
• Experience working with LLMs, vector databases, and prompt engineering.
• Knowledge of Docker, Kubernetes, and microservices architecture.
• Experience building and training AI models for domain-specific use cases.
• Ability to design and develop scalable, production-grade AI solutions.