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# Senior Agentic AI Engineer - Python (6+ Years Experience | Remote | Immediate Joiners)

- **Company:** AI, INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Databases, Continuous Integration, Graph Database, Python (Programming Language), NoSQL, Open Source Technology, Search Technologies, SQL Databases, Data Logging, Google Cloud, Enterprise Software Applications, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Security, Generative AI, Backend, Git, Fastapi, Build Management, Kubernetes, Low Latency, Deployment Automation, Machine Learning Operations, Virtual Agents, Restful APIs, GPT, Serverless Computing, Docker, Microservices
- **Published:** September 25, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pl4pxvcjjy

## About the Role

Our client is seeking a Senior Agentic AI Engineer with strong Python expertise to build and deploy production-grade Agentic AI and Generative AI solutions.

The ideal candidate should have strong hands-on experience with Python, AI Agents, RAG, LLMs, LangGraph/LangChain, APIs, and vector databases, with the ability to design scalable and reliable AI applications., * 6+ years of strong Python experience, including backend development, REST APIs, microservices, FastAPI, and/or Flask

 * Strong production experience with Agentic AI, AI Agents, tool calling, planning, reasoning, orchestration, memory/state management, and human-in-the-loop workflows
 * Strong experience with RAG, including ingestion, chunking, embeddings, semantic/vector search, hybrid retrieval, reranking, and hallucination reduction
 * Experience with OpenAI/GPT, Azure OpenAI, Claude, Gemini, AWS Bedrock, or open-source LLMs
 * Hands-on experience with LangGraph and/or LangChain
 * Experience with vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, Qdrant, Milvus, pgvector, Azure AI Search, or OpenSearch
 * Strong understanding of Prompt Engineering, Context Engineering, structured outputs, function calling, and guardrails
 * Strong knowledge of REST APIs, SQL/NoSQL, Git, Docker, CI/CD, monitoring, logging, and API security
 * Experience with AWS, Azure, or GCP
 * Experience with LLMOps/MLOps and production AI deployments

Nice to Have

 * Multi-agent systems
 * Agentic RAG / Advanced RAG
 * Graph RAG / Knowledge Graphs
 * AutoGen, CrewAI, or LlamaIndex
 * MCP (Model Context Protocol)
 * Kubernetes / Serverless
 * LLM evaluation and observability
 * AI guardrails and prompt-injection mitigation
 * Fine-tuning / LoRA / PEFT

Keywords

Python, Agentic AI, Generative AI, RAG, LLMs, LangGraph, LangChain, FastAPI, Flask, AI Agents, Tool Calling, Function Calling, Vector Databases, Pinecone, FAISS, ChromaDB, Qdrant, pgvector, OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock, AWS, Azure, GCP, REST APIs, Microservices, Docker, CI/CD, LLMOps, MLOps

Hashtags

#Python #AgenticAI #GenerativeAI #RAG #LLM #LangGraph #LangChain #FastAPI #AIAgents #VectorDatabase #OpenAI #AzureOpenAI #AWSBedrock #AWS #Azure #GCP #LLMOps #MLOps #AIEngineering #Hiring #YMindsAI

## Description

* Build and deploy Agentic AI and Generative AI applications
 * Develop scalable Python APIs, backend services, and microservices
 * Build AI agents with tool calling, function calling, planning, reasoning, memory, and multi-step workflows
 * Design and implement RAG pipelines and retrieval workflows
 * Develop stateful workflows using LangGraph / LangChain
 * Integrate agents with APIs, databases, vector stores, enterprise systems, and external tools
 * Work with OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock, and open-source LLMs
 * Deploy and optimize AI applications on AWS, Azure, or GCP
 * Implement evaluation, monitoring, observability, guardrails, and production best practices
 * Optimize applications for performance, latency, reliability, scalability, and cost