Senior Agentic AI Engineer – Python (4-7 Years Experience | Remote | Immediate Joiners)

YMinds.AI; Bengaluru; In loco; INR 2.500.000 - 4.500.000; Tempo pieno

Descrizione del lavoro

YMinds.AI is seeking a Senior Agentic AI Engineer with strong Python expertise to design and deploy production-grade Agentic AI and Generative AI solutions. You will build scalable Python APIs, backends, and microservices while crafting agents with tool calling, planning, and memory capabilities.

You will work with OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock, and open-source LLMs, deploying on AWS/Azure/GCP and ensuring observability, guardrails, and production best practices.

Competenze

• 4-7 years of Python and backend development experience.
• Hands-on experience with Agentic AI and AI agents, tool calling, planning, and human-in-the-loop workflows.
• Strong background in RAG pipelines, embeddings, and vector search.
• Experience with multiple LLMs (OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock or open-source).
• Proficiency with LangGraph/LangChain and vector databases.

Mansioni

• Build and deploy Agentic AI and Generative AI applications.
• Develop scalable Python APIs, backend services, and microservices.
• Create AI agents with tool calling, planning, memory, and multi-step workflows.
• Design RAG pipelines, retrieval workflows, and memory management.
• Integrate agents with APIs, databases, vector stores, and external tools.

Conoscenze

Python

Agentic AI

AI Agents

tool calling

planning

reasoning

memory/state management

multi-step workflows

RAG

LLMs

LangGraph/LangChain

APIs

vector databases

Pinecone

FAISS

ChromaDB

Weaviate

Qdrant

Milvus

pgvector

Azure/OpenAI

CI/CD

Docker

REST APIs

Git

Cloud platforms

LLMOps/MLOps

Strumenti

LangGraph

LangChain

Pinecone

FAISS

ChromaDB

Weaviate

Qdrant

Milvus

pgvector

Azure OpenAI

OpenAI GPT

Descrizione del lavoro

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, Azure AI services, APIs, and vector databases, with the ability to design scalable and reliable AI applications.

Key Responsibilities

• 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

Required Skills

• 4-7 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 LLMOps/MLOps and production AI deployments

Nice to Have

• Multi-agent systems
• Agentic RAG / Advanced RAG
• Graph RAG / Knowledge Graphs
• AutoGen, CrewAI, or LlamaIndex
• LLM evaluation and observability
• AI guardrails and prompt-injection mitigation