Are you passionate about Generative AI, Large Language Models (LLMs), RAG, Agentic AI, and production-grade MLOps? ๐
buzzhire is hiring an AI Engineer โ LLM & Production MLOps with 4.5โ8 years of experience to help build, optimize, and deploy scalable, real-world AI solutions.
This opportunity is ideal for professionals who have hands-on experience working with LLMs, RAG pipelines, autonomous agentic workflows, Python, Azure, Docker, Kubernetes, and MCP tools.
If you’re looking to work on production-focused AI systems and have strong experience across Generative AI and MLOps, this could be an opportunity worth exploring.
๐ Position Details
๐ข Company: buzzhire
๐ผ Position: AI Engineer (LLM & Production MLOps)
๐ Job ID: Not Mentioned
๐ Location: Not Mentioned
๐จโ๐ป Experience Required: 4.5โ8 Years
๐ Qualification: Not Mentioned
๐ข Employment Type: Not Mentioned
๐ Eligibility: Professionals with 4.5โ8 years of relevant experience and hands-on expertise in LLMs, RAG, Agentic AI, Python, Azure, Docker, Kubernetes, and MCP tools
๐ ๏ธ Skills Required
โ
Generative AI
โ
Large Language Models (LLMs)
โ
Retrieval-Augmented Generation (RAG)
โ
RAG Pipeline Development
โ
Agentic AI Workflows
โ
Multi-step Autonomous Agents
โ
Python
โ
Azure
โ
Docker
โ
Kubernetes
โ
MLOps
โ
DevOps
โ
Model Context Protocol (MCP) Tools Integration
โ
Production-grade AI Systems
โ
Problem Solving
โ
System Design & Engineering
๐ Position Overview
As an AI Engineer โ LLM & Production MLOps, you will work on transforming AI architecture and concepts into scalable, production-grade AI systems.
The role focuses on Generative AI and LLM-based applications, with particular emphasis on RAG pipelines and multi-step autonomous agentic systems.
Strong Python development skills are required, along with practical experience in cloud and production technologies such as Azure, Docker, and Kubernetes.
The position also requires proficiency in integrating and leveraging Model Context Protocol (MCP) tools, making it particularly relevant for professionals working on modern AI agent ecosystems.
๐ฏ Key Responsibilities
๐น Design and develop production-grade AI solutions using LLM technologies.
๐น Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
๐น Develop multi-step autonomous agentic AI workflows.
๐น Integrate LLMs into scalable real-world applications.
๐น Develop AI solutions using Python.
๐น Deploy and manage AI workloads on Azure.
๐น Containerize AI applications using Docker.
๐น Work with Kubernetes for scalable deployment and orchestration.
๐น Apply MLOps and DevOps practices to production AI systems.
๐น Integrate and leverage Model Context Protocol (MCP) tools.
๐น Optimize AI solutions for production environments.
๐น Translate architectural blueprints into scalable implementations.
๐ข About the Company
buzzhire is hiring for an AI Engineering position focused on LLMs and Production MLOps.
The available information specifically highlights work involving Generative AI, RAG, agentic workflows, Python, Azure, Docker, Kubernetes, and MCP tools.
Additional information about company size, products, clients, awards, benefits, or organizational culture is Not Mentioned in the provided job posting.
๐ฐ Salary Range
๐ต Salary: Not Mentioned
๐งช Expected Interview Rounds
The exact interview process has not been mentioned in the job posting.
Candidates may generally expect some combination of:
1๏ธโฃ Resume Shortlisting
2๏ธโฃ HR Screening
3๏ธโฃ AI/ML Technical Assessment
4๏ธโฃ Technical Interview โ LLMs & Generative AI
5๏ธโฃ Technical Interview โ RAG & Agentic AI
6๏ธโฃ MLOps/Cloud Discussion
7๏ธโฃ Managerial/Final Discussion
8๏ธโฃ HR Discussion & Documentation
โ ๏ธ These are general expected rounds, not confirmed buzzhire interview rounds.
๐ Preparation Tips
If you’re planning to apply, consider preparing the following areas:
โ Revise LLM fundamentals and modern Generative AI concepts.
โ Understand how RAG architectures work end-to-end.
โ Be prepared to explain RAG projects you have implemented.
โ Practice designing multi-step agentic workflows.
โ Understand autonomous AI agents and tool-calling concepts.
โ Strengthen your Python programming skills.
โ Revise Azure services relevant to AI/ML workloads.
โ Prepare Docker concepts and containerization workflows.
โ Revise Kubernetes fundamentals and deployment concepts.
โ Understand MLOps/DevOps practices for production AI systems.
โ Learn how MCP tools can be integrated into AI/agent workflows.
โ Be ready to explain how you would take an AI prototype into production.
๐ Resume Tips
โ Highlight hands-on Generative AI/LLM projects.
โ Clearly mention your experience with RAG pipelines.
โ Showcase agentic AI or autonomous workflow projects.
โ Mention Python development experience prominently.
โ Add relevant Azure experience.
โ Highlight Docker and Kubernetes expertise.
โ Mention MLOps/DevOps experience.
โ Include any practical MCP integration experience.
โ Quantify production impact wherever possible.
โ Tailor your resume specifically toward AI Engineer / LLM / MLOps requirements.
โ Keep your resume concise and technically focused.
โ Include relevant GitHub, portfolio, or project links where applicable.
โ Frequently Asked Questions (FAQs)
Q1. What position is buzzhire hiring for?
The company is hiring for an AI Engineer (LLM & Production MLOps) position.
Q2. What is the required experience?
The required experience is 4.5โ8 years.
Q3. Is this role suitable for freshers?
No. The provided requirement specifies 4.5โ8 years of experience.
Q4. What are the key AI skills required?
The role requires hands-on experience with LLMs, RAG pipelines, and multi-step autonomous agentic systems.
Q5. Which programming language is required?
Python is the core technology mentioned in the job posting.
Q6. Which cloud platform is required?
The job posting specifically mentions Azure.
Q7. Are Docker and Kubernetes required?
Yes. The posting specifically lists Docker and Kubernetes under the MLOps & DevOps requirements.
Q8. Is MCP experience required?
Yes. Proficiency in integrating and leveraging Model Context Protocol (MCP) tools is mentioned.
Q9. What is the salary for this position?
The salary is Not Mentioned in the provided job details.
Q10. How can candidates apply?
Interested candidates can drop their CV with Magh Sharma or email their CV to magh@buzzhire.in.
๐ Why Consider This Opportunity?
โจ Work on Generative AI and LLM-based solutions
โจ Exposure to RAG architectures
โจ Opportunity to work with agentic AI workflows
โจ Apply Python to real-world AI engineering
โจ Work with Azure cloud technologies
โจ Use Docker for AI application containerization
โจ Work with Kubernetes for scalable deployments
โจ Apply MLOps and DevOps practices
โจ Explore MCP tool integration
โจ Focus on production-grade AI systems
These points describe the technical opportunities indicated by the job description and should not be interpreted as guaranteed company benefits.
๐ฉ How to Apply
๐ง Email Your Resume: magh@buzzhire.in
๐ Suggested Subject Line: Application for AI Engineer (LLM & Production MLOps)
๐ Include in Your Email:
โข Position Applying For
โข Total Experience
โข Current Location
โข Notice Period
โข Updated Resume
โข Contact Number
โข Relevant LLM/Generative AI experience
โข RAG/Agentic AI project details
๐ข Share this opportunity with AI Engineers, Machine Learning Engineers, Generative AI professionals, LLM developers, MLOps engineers, and other job seekers who may be interested.
