Software Engineer (AI)

• *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**
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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**
• -------------------------

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.