Python / DS Engineer – AI / GenAI (7–9 Years)

Experience

7–9 Years

Location

Preferred: Hyderabad

Secondary: Bangalore

Candidates from other locations will be considered only if they demonstrate exceptional technical expertise. Hyderabad and Bangalore candidates are strongly preferred as the engineering team is being built in these locations.

Role Overview

We are looking for a highly skilled Python Backend Engineer with strong hands-on experience in building AI-powered applications. The ideal candidate should have practical implementation experience in GenAI, Agentic AI, RAG, MCP, and backend system development. This is a hands-on engineering role requiring deep technical expertise rather than conceptual knowledge.

Mandatory Technical Skills

Strong Core Python programming with backend development experience.

Hands-on experience designing and developing AI/GenAI applications.

Strong implementation experience with

AI Agents / Multi-Agent frameworks

Model Context Protocol (MCP)

Retrieval-Augmented Generation (RAG) and complex search implementations

LLM Gateway integrations

Microservices architecture

REST API development and third-party API integrations

Experience with system design and scalable backend architectures.

Good understanding of SDLC, Agile methodologies, and software engineering best practices.

Ability to explain design decisions, implementation approaches, and production use cases.

Interview Expectations

Candidates will be expected to demonstrate practical knowledge and hands-on implementation experience in the following areas:

Strong Python programming fundamentals and backend development.

Ability to explain and write Python code during technical discussions.

Hands-on implementation of RAG pipelines, including document ingestion, retrieval strategies,



embeddings, vector databases, and response generation.

Practical experience with MCP, including architecture, use cases, and implementation details.

Strong understanding of Agentic AI, agent orchestration, multi-agent workflows, planning, tool usage, and real-world implementation.

Experience integrating LLMs through APIs or gateways and building production-ready AI applications.

Experience designing and implementing microservices and REST APIs.

Ability to explain system design, architecture decisions, scalability considerations, and trade-offs.

Good understanding of Agile practices, SDLC, and engineering processes.

Important Screening Criteria for Vendors

Candidates should be submitted only if they have demonstrable hands-on experience in the mandatory technologies.

Please ensure candidates can:

Explain projects they have personally implemented.

Discuss architecture, design decisions, challenges, and solutions in detail.

Demonstrate robust Python programming fundamentals.

Explain RAG, MCP, Agentic AI, and LLM integrations beyond conceptual understanding.

Clearly differentiate between technologies they have implemented versus those they have only explored or supported.

Common Reasons for Rejection

Candidates are likely to be rejected if they:

Have resume claims that cannot be substantiated during technical interviews.

Provide only high-level or conceptual explanations of GenAI technologies.

Cannot demonstrate hands-on experience with Python, RAG, MCP, or Agentic AI.

Are unable to explain implementation details, design patterns, or production use cases.

Lack understanding of backend engineering fundamentals, SDLC, Agile practices, or system design.

Are unable to explain projects mentioned on their resume with sufficient technical depth.

📌 Python Backend Engineer (Hyderabad)
🏢 Globant
📍 Hyderabad