Junior Software Engineer (AI)

Role Overview

Key Responsibilities

• Develop and maintain GenAI application components using Python.
• Build REST APIs using FastAPI.
• Integrate applications with LLM APIs such as OpenAI and Azure OpenAI.
• Implement basic RAG pipelines for enterprise applications.
• Work with document processing, chunking, embeddings, and retrieval.
• Integrate applications with databases and external services.
• Write SQL queries and work with relational databases.
• Develop reusable and maintainable Python code.
• Write unit tests and integration tests for AI and backend components.
• Debug and resolve application, API, database, and LLM integration issues.
• Use Git for source-code management and collaborative development.
• Containerize applications using basic Docker practices.
• Support deployment and troubleshooting in development and test environments.
• Work with Senior Engineers to implement technical designs and AI workflows.
• Participate in code reviews, technical discussions, and documentation.
• Learn and adopt new GenAI frameworks and technologies as required.

Must-Have Skills

Programming & Backend

• Good hands-on knowledge of Python
• FastAPI
• REST API development
• SQL
• Understanding of backend application development
• Basic knowledge of API integration and error handling

Generative AI

• Basic understanding of Generative AI and LLMs
• Experience integrating LLM APIs
• Understanding of:
  • Prompts and prompt templates
  • Tokens and context
  • LLM parameters
  • Structured responses
  • Basic hallucination concepts

RAG

• Basic hands-on experience with Retrieval-Augmented Generation (RAG)
• Understanding of:
  • Document ingestion
  • Text chunking
  • Embeddings
  • Vector search
  • Context retrieval
  • Prompt/context construction

Development Tools

• Git / GitHub / Azure Repos
• Basic Docker
• Debugging and troubleshooting
• Basic software testing practices

Preferred Skills

• LangChain
• LangGraph
• Vector databases
• PostgreSQL
• Azure / Azure OpenAI
• OpenAI APIs
• Ollama
• Open-source LLMs
• MCP (Model Context Protocol)
• React / Next.js

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  Redis
• CI/CD
• Azure DevOps / GitHub Actions

Technical Expectations

• Develop a simple FastAPI endpoint.
• Integrate an LLM API into a Python application.
• Create a basic RAG workflow.
• Generate and use embeddings for document retrieval.
• Connect an application to PostgreSQL or another database.
• Write SQL queries for application requirements.
• Create basic prompt templates.
• Implement basic validation and error handling for LLM responses.
• Write unit tests for APIs and application components.
• Create a Dockerfile and run a Python application in Docker.
• Use Git effectively for branching, commits, pull requests, and code reviews.
• Debug API, database, and LLM integration issues.

Learning & Growth Expectations

• Advanced RAG
• Agentic AI
• LangGraph
• Multi-agent workflows
• LLM evaluation
• AI guardrails
• Vector and graph databases
• MCP
• Open-source LLMs
• Cloud-based AI deployment

What We Are Looking For

• Strong programming fundamentals.
• Good hands-on Python development experience.
• Genuine interest in Generative AI and LLM technologies.
• Ability to understand and implement technical requirements.
• Good debugging and problem-solving skills.
• Willingness to learn new AI frameworks and technologies.
• Ability to work effectively with Senior Engineers and Architects.
• Good communication and teamwork.
• Attention to code quality, testing, and documentation.