The Role
Develop enterprise GenAI applications using Python, FastAPI, LLM APIs, RAG pipelines, embeddings, vector databases, and AI-agent workflows. Integrate providers such as OpenAI, Azure OpenAI, Anthropic, and Gemini; build PostgreSQL-backed services; containerize applications with Docker; and support testing, deployment, troubleshooting, performance optimization, documentation, and technical evaluations.
Summary Generated by Built In
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.
- Python — strong hands-on experience
- FastAPI
- REST API development
- Experience developing backend applications
- Understanding of API authentication, error handling, and integration
- 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
- 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
- PostgreSQL
- Git / GitHub / Azure Repos
- Docker
- Basic Azure or AWS experience
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
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.
- 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.
Skills Required
- 3-4 years of software engineering experience
- Strong hands-on Python experience
- FastAPI experience
- REST API and backend application development experience
- Understanding of API authentication, error handling, and integrations
- Understanding of Generative AI and LLMs
- Experience with LLM APIs
- Prompt engineering experience
- Understanding of tokens, context windows, temperature, and embeddings
- Experience with one or more LLM providers, including OpenAI, Azure OpenAI, Anthropic, or Google Gemini
- Practical Retrieval-Augmented Generation experience
- Experience with embeddings, semantic search, vector databases, document ingestion, chunking, retrieval, and context construction
- PostgreSQL experience
- Git-based development experience using Git, GitHub, or Azure Repos
- Docker experience
- Basic Azure or AWS experience
- LangChain or LangGraph exposure
- Neo4j or GraphRAG exposure
- Model Context Protocol exposure
- Ollama or open-source LLM exposure
- React or Next.js experience
- Redis experience
- CI/CD, Azure DevOps, or GitHub Actions experience
- Understanding of LLM evaluation and AI guardrails
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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.
