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 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.