Python AI Engineer
Bramkas
- Location
- US
- Workplace
- Remote
- Employment
- Full Time
- Salary
- —
Posted 2mo ago
The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.
Responsibilities
- Design, develop, and maintain enterprise AI applications using Python, React, and MongoDB.
- Build scalable Retrieval-Augmented Generation (RAG) solutions using vector databases and embedding models.
- Develop end-to-end document processing pipelines, including document parsing, extraction, chunking, indexing, and semantic retrieval.
- Integrate commercial and open-source Large Language Models (LLMs).
- Build real-time AI applications using Streaming APIs and Server-Sent Events (SSE).
- Implement document citation, source attribution, and grounded AI responses.
- Design and develop Agentic AI solutions using multi-agent frameworks and Model Context Protocol (MCP).
- Develop REST APIs and microservices supporting AI workflows.
- Collaborate with architects, product owners, and engineering teams to deliver scalable AI solutions.
- Participate in architecture reviews, code reviews, and Agile development activities.
Requirements
- 3+ years of experience developing enterprise applications using Python, React, and MongoDB.
- Strong hands-on experience with Generative AI (GenAI).
- Strong hands-on experience with Large Language Models (LLMs).
- Strong hands-on experience with Retrieval-Augmented Generation (RAG).
- Strong hands-on experience with Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB, pgvector, or similar).
- Strong hands-on experience with Embeddings and semantic search.
- Strong hands-on experience with Retrieval optimization.
- Experience building document parsing, OCR, extraction, indexing, and document ingestion pipelines.
- Experience integrating LLM APIs (OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source models).
- Experience implementing Streaming APIs and Server-Sent Events (SSE).
- Experience implementing document citation and reference generation.
- Experience designing and developing Agentic AI solutions using multi-agent frameworks and MCP.
- Strong experience with REST APIs.
- Strong experience with Microservices.
- Strong experience with Git.
- Strong experience with Agile/Scrum.
- Minimum 5 years of software development experience.
- Minimum 2 years of hands-on experience building production AI/LLM applications.
Preferred
- Experience with AI orchestration frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or LangChain.
- Experience building AI workflow automation and agent orchestration.
- Familiarity with Langfuse, Arize AI, or similar AI observability and evaluation platforms.
- Experience with Docker.
- Experience with Kubernetes.
- Experience with CI/CD.
- Knowledge of Terraform.
- Knowledge of Infrastructure as Code (IaC).
- Knowledge of DevOps.
- Experience with AWS.
- Experience with Azure.
- Experience with Google Cloud Platform.
Skills
- Python
- React
- MongoDB
- Generative AI
- GenAI
- LLM
- RAG
- Agentic AI
- Vector Databases
- MCP
- Pinecone
- Weaviate
- Milvus
- ChromaDB
- pgvector
- OpenAI
- Azure OpenAI
- Anthropic
- Gemini
- Git
- Docker
- Kubernetes
- CI/CD
- Terraform
- IaC
- DevOps
- AWS
- Azure
- Google Cloud Platform
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel
- LangChain
- Langfuse
- Arize AI
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