About this role
**Role Overview**
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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**
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- 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**
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### **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**
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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**
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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**
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- 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.