The Role
Develop and maintain Generative AI applications using Python, FastAPI, LLM APIs, and RAG pipelines. Build integrations, REST APIs, database functionality, embeddings and retrieval workflows, automated tests, and Dockerized services. Troubleshoot application, API, database, and LLM issues while collaborating with senior engineers and architects. Participate in code reviews, documentation, and technical discussions while growing expertise in agentic AI, LangGraph, evaluation, guardrails, and cloud-based AI deployment.
Summary Generated by Built In
Role Overview
We are looking for a Junior AI Engineer — GenAI Application Development with 2–3 years of software development experience and an interest in building practical Generative AI applications.
The candidate will work closely with Senior AI Engineers and AI Architects to develop and maintain GenAI application components, APIs, RAG pipelines, integrations, and automated tests.
This role is ideal for a software engineer who has a strong foundation in Python and backend development and is looking to build deeper expertise in LLMs, RAG, AI Agents, and modern AI application development.
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.
- Good hands-on knowledge of Python
- FastAPI
- REST API development
- SQL
- Understanding of backend application development
- Basic knowledge of API integration and error handling
- 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
- Basic hands-on experience with Retrieval-Augmented Generation (RAG)
- Understanding of:
- Document ingestion
- Text chunking
- Embeddings
- Vector search
- Context retrieval
- Prompt/context construction
- Git / GitHub / Azure Repos
- Basic Docker
- Debugging and troubleshooting
- Basic software testing practices
Exposure to any of the following will be an advantage:
- LangChain
- LangGraph
- Vector databases
- PostgreSQL
- Azure / Azure OpenAI
- OpenAI APIs
- Ollama
- Open-source LLMs
- MCP (Model Context Protocol)
- React / Next.js
- Redis
- CI/CD
- Azure DevOps / GitHub Actions
The candidate should be able to perform tasks such as:
- 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.
This role provides an opportunity to grow from GenAI application development toward advanced AI engineering.
The candidate is expected to progressively develop skills in:
- Advanced RAG
- Agentic AI
- LangGraph
- Multi-agent workflows
- LLM evaluation
- AI guardrails
- Vector and graph databases
- MCP
- Open-source LLMs
- Cloud-based AI deployment
- 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.
Skills Required
- 2-3 years of software development experience
- Hands-on knowledge of Python
- FastAPI experience
- REST API development experience
- SQL knowledge
- Backend application development knowledge
- Basic API integration and error handling knowledge
- Basic understanding of Generative AI and LLMs
- Experience integrating LLM APIs
- Understanding of prompts, tokens, context, LLM parameters, structured responses, and hallucinations
- Basic hands-on experience with Retrieval-Augmented Generation
- Understanding of document ingestion, text chunking, embeddings, vector search, context retrieval, and prompt construction
- Experience with Git, GitHub, or Azure Repos
- Basic Docker knowledge
- Debugging and troubleshooting skills
- Basic software testing practices
- LangChain exposure
- LangGraph exposure
- Vector database exposure
- PostgreSQL exposure
- Azure or Azure OpenAI exposure
- OpenAI API exposure
- Ollama exposure
- Open-source LLM exposure
- MCP exposure
- React or Next.js exposure
- Redis exposure
- CI/CD exposure
- Azure DevOps or GitHub Actions exposure
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The Company
What We Do
Dash Technologies Inc. is a software development and IT services company that helps businesses modernize, scale, and grow. It builds enterprise-grade mobile, web, and custom software, while specializing in device engineering and AI/ML analytics. The company also develops healthcare products and describes its work as transforming healthcare delivery globally for organizations across multiple industries, combining technology expertise with customer-focused support.
