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.

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