AI Engineer - Generative AI, RAG & Backend

Role Overview

We are looking for an AI Engineer — GenAI, RAG & Backend with 3–4 years of software engineering experience and hands-on exposure to Generative AI, LLM applications, RAG, APIs, and AI-agent workflows.

The candidate will work closely with the Senior AI/ML Engineer and AI Architect to develop, integrate, test, and deploy enterprise-grade AI applications.

The ideal candidate should have a strong foundation in Python backend development and practical knowledge of modern GenAI technologies.

Key Responsibilities

• Develop and maintain Generative AI applications using Python and LLM APIs.
• Build and enhance RAG pipelines using embeddings, vector databases, retrieval, and contextual generation.
• Develop backend services and APIs using Python, FastAPI, and REST APIs.
• Integrate LLM providers such as OpenAI, Azure OpenAI, Anthropic, and Gemini.
• Implement prompt engineering techniques for reliable and consistent LLM responses.
• Develop basic AI-agent and tool-calling workflows under the guidance of senior engineers.
• Work with vector databases for semantic search and knowledge retrieval.
• Design and implement database integrations using PostgreSQL.
• Write reusable, maintainable, and well-tested Python code.
• Containerize applications using Docker and support cloud deployment.
• Troubleshoot API, LLM, RAG, and application-level issues.
• Participate in code reviews, technical discussions, testing, and documentation.
• Work with the Senior Engineer and AI Architect to implement technical designs and AI workflows.
• Perform POCs and evaluate new GenAI technologies when required.
• Monitor and improve application performance, reliability, response quality, and LLM usage costs.

Must-Have Skills

Programming & Backend

• Python — strong hands-on experience
• FastAPI
• REST API development
• Experience developing backend applications
• Understanding of API authentication, error handling, and integration

Generative AI & LLM

• Understanding of Generative AI and LLMs
• Experience working with LLM APIs
• Prompt engineering
• Understanding of tokens, context windows, temperature, embeddings, etc.
• Experience with one or more LLM providers such as:
  • OpenAI
  • Azure OpenAI
  • Anthropic
  • Google Gemini

RAG & Vector Search

• Practical experience with Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic search
• Vector databases
• Document ingestion and chunking
• Retrieval and context construction
• Basic understanding of RAG quality and hallucination issues

Database & Development Tools

• PostgreSQL
• Git / GitHub / Azure Repos
• Docker
• Basic Azure or AWS experience

Preferred Skills

Candidates with exposure to the following will be preferred:

• LangChain
• LangGraph
• Neo4j / GraphRAG
• Model Context Protocol (MCP)
• Ollama
• Open-source LLMs
• Azure OpenAI
• React / Next.js
• Redis
• CI/CD
• Azure DevOps / GitHub Actions
• Basic understanding of LLM evaluation and AI guardrails

Technical Expectations

The candidate should be able to independently handle tasks such as:

• Creating a FastAPI service for an LLM application.
• Integrating an LLM API into an existing application.
• Building a basic RAG pipeline from document ingestion to response generation.
• Creating embeddings and storing/searching them in a vector database.
• Implementing prompt templates and structured LLM responses.
• Building basic tool-calling or agent workflows.
• Connecting AI services with PostgreSQL and other backend systems.
• Dockerizing a Python application.
• Debugging API and LLM integration issues.
• Writing unit tests and maintaining clean Git-based development practices.

What We Are Looking For

• Strong software development fundamentals with practical Python experience.
• Genuine hands-on experience building GenAI applications, not just theoretical knowledge.
• Good understanding of RAG concepts and LLM application patterns.
• Ability to learn new AI frameworks and technologies quickly.
• Good debugging and problem-solving skills.
• Ability to work effectively under the guidance of a Senior Engineer and AI Architect.
• Good communication and collaboration skills.
• Willingness to work on both AI/ML and backend engineering tasks.