Applied AI Engineer

Meta; Bengaluru; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Meta in Bengaluru seeks a backend engineer with deep Python experience to design and build scalable AI-powered services. You will craft production-grade APIs, work with LLMs, RAG systems, embeddings, and orchestration components, while owning features end-to-end from design through deployment.

The role emphasizes strong backend fundamentals, distributed systems thinking, and collaboration with platform teams to ship reliable AI-powered solutions in a cloud-based environment.

Qualifications

• Strong Python programming experience and backend development.
• Experience building production APIs and services.
• Hands-on experience with LLMs / Generative AI applications.
• Experience with RAG, vector databases, embeddings, agents or LLM orchestration.
• Familiar with FastAPI / Flask / Django or similar backend frameworks.
• Strong understanding of REST APIs, databases, system design and distributed systems.
• Experience with Docker and cloud platforms such as AWS / GCP / Azure.
• Proven problem-solving and debugging skills.
• Ability to own a problem from design to deployment to production.

Skills

Python programming

Backend development

LLMs / Generative AI

RAG / vector databases

REST API design

System design

Distributed systems

Docker

Cloud platforms (AWS/GCP/Azure)

Debugging

Ownership mindset

Tools

FastAPI

Flask

Django

Job description

Requirements:

• Strong hands-on programming experience in Python.
• Strong experience building backend services, APIs and production systems.
• Hands-on experience with LLMs / Generative AI / AI applications.
• Experience with RAG, vector databases, embeddings, agents or LLM orchestration.
• Experience with FastAPI / Flask / Django or similar backend frameworks.
• Good understanding of REST APIs, databases, system design and distributed systems.
• Experience with Docker and cloud platforms such as AWS / GCP / Azure.
• Strong problem-solving and debugging skills.
• Ability to take ownership of a problem from design, development, and deployment to production.