Lead AI Automation Engineer (LLM & GPU Infrastructure)

We are looking for an experienced AI Infrastructure & Automation Engineer to join our team. In this role, you will serve as the critical link between advanced AI application logic and the hardware that supports it. Your primary responsibility will be to scale AI operations by ensuring that Large Language Model (LLM) workflows run efficiently on centralized GPU infrastructure. Additionally, you will focus on automating backend systems to streamline processes and improve overall performance. If you are passionate about building reliable AI pipelines and optimizing server environments, this opportunity is for you.

Key Responsibilities

Infrastructure Management: Design, deploy, and maintain centralized GPU server clusters to guarantee high availability and optimal performance for LLM training and inference tasks.

LLM Integration: Develop and maintain backend systems that integrate Large Language Models into production-ready applications, ensuring smooth and reliable operation.

Automation: Create end-to-end automation workflows to minimize manual operational tasks, leveraging tools such as N8N, Make, or custom Python scripts.

System Optimization: Continuously monitor server resources and fine-tune configurations to maximize GPU utilization and reduce inference latency.

Pipeline Development: Build and manage data pipelines for data ingestion, preprocessing, and model fine-tuning to support AI workflows.

Scalability: Identify and resolve hardware and software bottlenecks, scaling infrastructure effectively to meet growing AI service demands.

Required Qualifications

Deep Technical Expertise: Minimum of 3 years’ experience in AI/ML infrastructure, systems engineering, or a related technical field.

GPU & Server Knowledge: Proven hands-on experience managing GPU-accelerated servers, including familiarity with NVIDIA technologies, CUDA, and containerization platforms such as Docker and Kubernetes.

LLM Proficiency: Practical experience deploying and fine-tuning Large Language Models like Llama, OpenAI API, or Mistral in production environments.

Programming Skills: Strong proficiency in Python, along with experience working with MLOps frameworks.

System Integration: Demonstrated ability to connect APIs, webhooks, and databases to build cohesive and automated business workflows.

Problem-Solving: A proactive “builder” mindset with the capability to identify infrastructure bottlenecks and implement effective solutions.

This is a full-time, in-person position offering a competitive monthly salary ranging from Rs80,000 to Rs150,000, commensurate with experience and skills. If you are eager to contribute to cutting-edge AI infrastructure and automation projects, we encourage you to apply.