Job Title: DevOps Engineer (LLM & AI Infrastructure)
Experience Required: 3–4 Years
Role Overview:
We are looking for a DevOps Engineer who understands not just systems, but intelligence at scale — someone who can bridge infrastructure with modern AI workflows. This role focuses on building, managing, and optimizing environments for Large Language Models, image models, and high-performance GPU workloads.
Key Responsibilities:
- Design, deploy, and manage scalable infrastructure for AI/ML workloads
- Work with GPU-based systems and optimize performance for model training and inference
- Build and maintain CI/CD pipelines for ML models and backend services
- Deploy and manage containerized applications using Docker and Kubernetes
- Handle orchestration of distributed systems and model serving pipelines
- Optimize CUDA environments and GPU utilization
- Manage cloud and on-prem compute clusters for AI workloads
- Collaborate with ML engineers to productionize LLMs and LoRA-based fine-tuned models
- Monitor system performance, logs, and reliability across services
Required Skills:
- Strong proficiency in Python
- Solid understanding of DevOps principles and infrastructure automation
- Hands-on experience with Docker and Kubernetes
- Experience with GPU systems, CUDA, and high-performance computing
- Understanding of how Large Language Models (LLMs) work
- Familiarity with LoRA (Low-Rank Adaptation) and model fine-tuning concepts
- Knowledge of model deployment and inference pipelines
- Experience with cloud platforms (AWS / GCP / Azure)
Good to Have:
- Experience with model serving frameworks (Triton, TorchServe, vLLM, etc.)
- Familiarity with Replicate or similar model hosting platforms
- Knowledge of distributed training and inference optimization
- Exposure to vector databases and retrieval systems
Pay: ₹20,000.00 - ₹60,000.00 per month
Experience:
- LLM: 3 years (Required)
- Ai architecture: 3 years (Required)
- DevOps engineer: 3 years (Required)
Work Location: In person
