Machine Learning

Job description

The role involves developing, training, testing, evaluating, and deploying AI/ML models and Generative AI applications. Design and implement LLM‑based solutions and optimize inference performance. Build and manage scalable AI infrastructure using GCP, OpenShift, Kubernetes, and Docker. Implement MLOps and LLMOps pipelines to automate model development, deployment, monitoring, and maintenance. Optimize LLM inference through distributed computing, benchmarking, and scaling to meet SLAs. Manage GPU workloads, distributed processing, and autoscaling for AI applications. Work with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments including HPC, Slurm, and Kubernetes. Develop AI application architecture and manage end‑to‑end workflows. Implement monitoring dashboards and performance tracking for ML/LLM workloads. Establish CI/CD pipelines using Argo CD, Git, and Jenkins. Collaborate with cross‑functional teams and manage project activities to deliver scalable AI solutions

Requirements

• strong expertise in Artificial Intelligence (AI)
• strong expertise in Machine Learning (ML)
• strong expertise in Generative AI (GenAI)
• strong expertise in Large Language Models (LLMs)
• hands‑on experience with cloud technologies GCP, OpenShift, Kubernetes, and Docker
• practical knowledge of MLOps, LLMOps, LLMs, and Generative AI
• experience with monitoring, dashboards, AI application architecture, and end‑to‑end workflows
• knowledge of DevOps practices and CI/CD tools including Argo CD, Git, and Jenkins
• proficient in Excel
• proficient in Python
• proficient in SQL databases
• proficient in Docker
• more than 5 years of relevant professional experience in Data Science, AI, or Machine Learning
• bachelor's degree in Computer Science or related field
• strong understanding of AI/ML model training, testing, evaluation, and deployment
• understanding of LLM architecture, inference mechanics, and performance optimization
• experience in inference scaling, distributed computing, benchmarking, and GPU workloads
• familiarity with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments

Skills