Verified Job
IT / Software / Data Analyst
AI Engineer - Deep Learning/Machine Learning
IT / Software / Data Analyst
#746255
Remote / WFH
Experis IT Private Limited · Bengaluru, Karnataka ·
Job Description
Job description
Role Overview:We are seeking an experienced Agentic AI Engineer with strong expertise in Python and modern Generative AI frameworks. The ideal candidate will have hands-on experience designing, developing, and deploying enterprise-grade AI applications using LLMs, RAG architectures, and multi-agent systems. The candidate should be comfortable building intelligent autonomous workflows, integrating external tools, and deploying scalable AI solutions in cloud environments.Key Responsibilities:- Design, develop, and deploy Generative AI and Agentic AI solutions using Python.- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.- Develop multi-agent workflows using LangGraph, including agent orchestration, state management, and memory handling.- Create AI applications using LangChain and LangFlow for rapid development and workflow automation.- Implement tool calling, function calling, API integrations, and external system interactions within AI agents.- Design and implement MCP (Model Context Protocol) servers and integrations.- Develop autonomous AI agents capable of reasoning, planning, task execution, and memory sharing.- Deploy and monitor LLM-based applications in production environments.- Integrate AI solutions with enterprise systems, APIs, databases, and cloud services.- Collaborate with data scientists, software engineers, and business stakeholders to identify and implement AI use cases.- Ensure scalability, performance, observability, and security of AI applications.- Participate in architecture discussions, code reviews, and technical mentoring.Mandatory Skills:- Strong programming experience in Python.- Hands-on experience with LangChain.- Hands-on experience with LangGraph.- Hands-on experience with LangFlow.- Experience building and deploying LLM-based applications.- Strong understanding of RAG (Retrieval-Augmented Generation) architecture.- Experience building Agentic AI systems and autonomous agents.- Hands-on experience with:1. Multi-Agent Systems2. Tool Calling / Function Calling3. MCP (Model Context Protocol)4. Agent Memory Management5. Memory Sharing Across Agents6. Workflow Orchestration- Experience with vector databases such as Pinecone, ChromaDB, Weaviate, or FAISS.- Experience integrating OpenAI, Claude, Gemini, Llama, Mistral, or similar models.- Understanding of prompt engineering and LLM evaluation techniques.Good to Have:- Exposure to Machine Learning and Deep Learning concepts.- Experience with model fine-tuning techniques such as LoRA, QLoRA, or PEFT.- Knowledge of Hugging Face ecosystem.- Experience with cloud platforms such as AWS, Azure, or GCP.- Experience with Docker, Kubernetes, and CI/CD pipelines.- Knowledge of MLOps practices and model monitoring.- Experience with AI observability tools such as Langfuse, Arize, or Phoenix. (ref:hirist.tech)
Role Overview:We are seeking an experienced Agentic AI Engineer with strong expertise in Python and modern Generative AI frameworks. The ideal candidate will have hands-on experience designing, developing, and deploying enterprise-grade AI applications using LLMs, RAG architectures, and multi-agent systems. The candidate should be comfortable building intelligent autonomous workflows, integrating external tools, and deploying scalable AI solutions in cloud environments.Key Responsibilities:- Design, develop, and deploy Generative AI and Agentic AI solutions using Python.- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.- Develop multi-agent workflows using LangGraph, including agent orchestration, state management, and memory handling.- Create AI applications using LangChain and LangFlow for rapid development and workflow automation.- Implement tool calling, function calling, API integrations, and external system interactions within AI agents.- Design and implement MCP (Model Context Protocol) servers and integrations.- Develop autonomous AI agents capable of reasoning, planning, task execution, and memory sharing.- Deploy and monitor LLM-based applications in production environments.- Integrate AI solutions with enterprise systems, APIs, databases, and cloud services.- Collaborate with data scientists, software engineers, and business stakeholders to identify and implement AI use cases.- Ensure scalability, performance, observability, and security of AI applications.- Participate in architecture discussions, code reviews, and technical mentoring.Mandatory Skills:- Strong programming experience in Python.- Hands-on experience with LangChain.- Hands-on experience with LangGraph.- Hands-on experience with LangFlow.- Experience building and deploying LLM-based applications.- Strong understanding of RAG (Retrieval-Augmented Generation) architecture.- Experience building Agentic AI systems and autonomous agents.- Hands-on experience with:1. Multi-Agent Systems2. Tool Calling / Function Calling3. MCP (Model Context Protocol)4. Agent Memory Management5. Memory Sharing Across Agents6. Workflow Orchestration- Experience with vector databases such as Pinecone, ChromaDB, Weaviate, or FAISS.- Experience integrating OpenAI, Claude, Gemini, Llama, Mistral, or similar models.- Understanding of prompt engineering and LLM evaluation techniques.Good to Have:- Exposure to Machine Learning and Deep Learning concepts.- Experience with model fine-tuning techniques such as LoRA, QLoRA, or PEFT.- Knowledge of Hugging Face ecosystem.- Experience with cloud platforms such as AWS, Azure, or GCP.- Experience with Docker, Kubernetes, and CI/CD pipelines.- Knowledge of MLOps practices and model monitoring.- Experience with AI observability tools such as Langfuse, Arize, or Phoenix. (ref:hirist.tech)
