AI Engineer

Company: Go Digit General Insurance Limited

Location: Bengaluru

Work Mode: -site, five days a week

About the Role We are looking for a Software Engineer - AI Engineering to independently build and deliver complex AI solutions while serving as a key technical contributor within an AI delivery pod.

This role requires strong development expertise, ownership of critical modules, and the ability to drive implementations with minimal supervision.

Key Responsibilities

• Design and develop complex AI applications, services, APIs, and integrations.
• Build scalable solutions using LLMs, RAG, Agentic AI, MCP, Voice AI, Computer Vision, and Intelligent Document Processing technologies.
• Own and deliver critical modules and technical components.
• Contribute to technical design discussions and solution implementation.
• Review code and support engineering quality initiatives.
• Mentor and support junior engineers on design, coding, and debugging practices.
• Optimize applications for performance, scalability, reliability, and maintainability.
• Build reusable frameworks, libraries, and accelerators.
• Support production releases, monitoring, troubleshooting, and root-cause analysis.
• Collaborate with Technical Leads on solution execution and sprint delivery.

Required Experience

• 2-3 years of experience in Software Engineering, AI/ML Engineering, or GenAI solution development.
• Experience building and deploying production-grade applications.
• Strong expertise in Python and backend application development.
• Experience with LLMs, RAG, Prompt Engineering, Agentic AI, or AI integrations.
• Experience with FastAPI, LangChain, LangGraph, APIs, and Microservices.
• Understanding of distributed systems, SQL, CI/CD, Docker, and cloud platforms.
• Strong debugging, optimization, and production support capabilities.
• Ability to independently own and deliver complex technical implementations.

Success Measures

• Successful delivery of complex AI capabilities.
• High engineering quality and maintainability.
• Contribution to reusable frameworks and shared components.
• Effective mentoring support within the pod.
• Reliable, scalable, and production-ready solutions.
• Contribution to engineering standards and technical excellence