Live opening · Posted 14 days ago
The key details from the original listing.
Live timing from JobBeeper.
Description supplied by the original job listing.
About the Role
Our client is seeking a Senior Agentic AI Engineer with strong Python expertise to design, build, and deploy production-grade Agentic AI and Generative AI solutions.
The role requires strong hands-on experience in Python, Agentic AI, RAG, LLMs, LangGraph/LangChain, APIs, and vector databases, with a focus on building scalable and reliable AI applications for production environments.
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 agent 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 monitoring, evaluation, observability, LLMOps/MLOps, and production best practices
Follow engineering best practices around Git, CI/CD, Docker, testing, documentation, and deployment
Required Skills – Must Have
Python: 5+ years of strong hands-on experience with Python, backend systems, REST APIs, microservices, integrations, FastAPI, and/or Flask
Agentic AI: Strong production experience with AI Agents, autonomous/semi-autonomous workflows, tool calling, function calling, planning, reasoning, memory/state management, agent orchestration, retries, and human-in-the-loop workflows
RAG: Strong experience with Retrieval-Augmented Generation (RAG), including document ingestion, chunking, embeddings, semantic/vector search, hybrid retrieval, reranking, contextual grounding, RAG evaluation, and hallucination reduction
LLMs / Generative AI: Production experience with OpenAI/GPT, Azure OpenAI, Claude, Gemini, AWS Bedrock, or open-source LLMs
LangGraph / LangChain: Hands-on experience with LangGraph, LangChain, agent development, stateful workflows, tool orchestration, RAG pipelines, memory, conditional routing, and multi-step execution
Vector Databases: Experience with Pinecone, FAISS, ChromaDB, Weaviate, Qdrant, Milvus, pgvector, Azure AI Search, or OpenSearch
Prompt & Context Engineering: Strong understanding of structured prompting, system prompting, few-shot prompting, function calling, tool calling, structured outputs, context engineering, prompt evaluation, and guardrails
AI Architecture: Ability to design and explain end-to-end Agentic AI architectures covering LLMs, agents, RAG, tools/APIs, vector stores, memory/state, orchestration, enterprise systems, evaluation, monitoring, and deployment
Strong knowledge of REST APIs, SQL/NoSQL databases, Git, Docker, CI/CD, API Security, Monitoring, and Logging
Experience with AWS, Azure, or GCP
Experience with LLMOps, MLOps, and production deployments
Nice to Have
Multi-agent systems
Agentic RAG / Advanced RAG
Graph RAG / Knowledge Graphs
Hybrid search and advanced reranking
AutoGen, CrewAI, or LlamaIndex
MCP (Model Context Protocol)
Kubernetes
Serverless deployments
LLM evaluation frameworks
AI guardrails
LLM observability and tracing
Fine-tuning / LoRA / PEFT
AI security and prompt-injection mitigation
What We Are Looking For
Candidates should have genuine hands-on production experience and be able to discuss:
Agentic AI solutions they personally built
Agent architecture and orchestration
Multi-step workflows and tool calling
RAG architecture and retrieval strategies
LangGraph/LangChain implementation
Memory and state management
LLM and vector database selection
API and enterprise integrations
Production deployment architecture
Evaluation and hallucination mitigation
Performance, latency, reliability, and cost optimization
Challenges encountered while moving AI solutions from POC to production
About YMinds.AI
YMinds.AI builds production-ready AI solutions for global clients, with a focus on Agentic AI, Generative AI, intelligent automation, scalable AI architectures, and real-world business impact.
We work with multiple clients seeking experienced Agentic AI and Python engineering professionals across different products, industries, and enterprise AI use cases.
Keywords
Python, Agentic AI, Generative AI, RAG, LLMs, LangGraph, LangChain, FastAPI, Flask, REST APIs, Microservices, AI Agents, Tool Calling, Function Calling, Planning, Reasoning, Memory Management, State Management, Agent Orchestration, Vector Databases, Pinecone, FAISS, ChromaDB, Weaviate, Qdrant, Milvus, pgvector, Azure AI Search, OpenSearch, Prompt Engineering, Context Engineering, OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock, AWS, Azure, GCP, SQL, NoSQL, Docker, Git, CI/CD, API Security, Monitoring, Logging, LLMOps, MLOps, Production Deployment
Hashtags
#Python #AgenticAI #GenerativeAI #RAG #LLM #LangGraph #LangChain #FastAPI #Flask #AIAgents #VectorDatabase #PromptEngineering #ContextEngineering #ToolCalling #OpenAI #AzureOpenAI #AWSBedrock #AWS #Azure #GCP #Microservices #RESTAPI #Docker #CICD #LLMOps #MLOps #AIEngineering #Hiring #YMindsAI
Work arrangement
No
Recently tracked roles with full details and direct application links.