> DOU
AI Engineer
pythonsqlazure openaiclaude apiopenaiany one ofazure openairagembeddingsdockerci/cd
fastapifaissweaviateprompt engineeringlangchain
semantic dedup
Reposted 2× across Djinni + DOU — deduped to one listing.
metahunt matched 2 postings of this exact role by semantic similarity, so you see it once instead of 2 times.
> full description
Tech Stack:
∙ Python (5+ years)
∙ Strong working knowledge of SQL for data extraction and analysis (3+ years preferred)
∙ API integration with LLMs: OpenAI, Azure OpenAI, Claude
∙ ML/LLM architectures: RAG, embeddings, retrievers
∙ DevOps/MLOps: Docker, deployment of models on dedicated servers or cloud infrastructure
∙ Nice to have: FastAPI, vector databases (FAISS, Weaviate), prompt engineering
∙ Tools under consideration: LangChain or similar frameworks for rapid prototyping of LLM-based pipelines
Responsibilities:
∙ Design and build a production-grade LLM+RAG system for contextual data analysis
∙ Integrate external data sources and tools into the assistant’s knowledge base
∙ Set up and fine-tune LLMs on dedicated infrastructure or in the cloud
∙ Develop complex multi-step agents and orchestrated pipelines
∙ Build integrations with different platforms via API
∙ Ensure performance, scalability, and CI/CD automation of the ML systems
Requirements:
∙ Proven experience building real-world AI/ML products
∙ Deep understanding of the full machine learning lifecycle
∙ Experience working with popular LLM APIs and frameworks
∙ Ability to work independently and propose technical solutions
∙ Hands-on experience with LLM+RAG design patterns and prompt engineering
∙ Python (5+ years)
∙ Strong working knowledge of SQL for data extraction and analysis (3+ years preferred)
∙ API integration with LLMs: OpenAI, Azure OpenAI, Claude
∙ ML/LLM architectures: RAG, embeddings, retrievers
∙ DevOps/MLOps: Docker, deployment of models on dedicated servers or cloud infrastructure
∙ Nice to have: FastAPI, vector databases (FAISS, Weaviate), prompt engineering
∙ Tools under consideration: LangChain or similar frameworks for rapid prototyping of LLM-based pipelines
Responsibilities:
∙ Design and build a production-grade LLM+RAG system for contextual data analysis
∙ Integrate external data sources and tools into the assistant’s knowledge base
∙ Set up and fine-tune LLMs on dedicated infrastructure or in the cloud
∙ Develop complex multi-step agents and orchestrated pipelines
∙ Build integrations with different platforms via API
∙ Ensure performance, scalability, and CI/CD automation of the ML systems
Requirements:
∙ Proven experience building real-world AI/ML products
∙ Deep understanding of the full machine learning lifecycle
∙ Experience working with popular LLM APIs and frameworks
∙ Ability to work independently and propose technical solutions
∙ Hands-on experience with LLM+RAG design patterns and prompt engineering