Platform Engineer

Experience:

3+ years of experience in platform engineering, DevOps, or systems engineering, with a strong focus on machine learning and AI workloads.

Proven experience working with LLM workflows, and GPU-based machine learning infrastructure.

Hands-on experience in managing distributed computing systems, training large-scale models, and deploying AI systems in cloud environments.

Strong knowledge of GPU architectures (e.g., NVIDIA A100, V100, etc.), multi-GPU systems, and optimization techniques for AI workloads.

Technical Skills:

Proficiency in Linux systems and command-line tools. Strong scripting skills (Python, Bash, or similar).

Expertise in containerization and orchestration technologies (e.g., Docker, Kubernetes, Helm). Experience with cloud platforms (AWS, GCP, Azure), tools such as Terraform, /Terragrunt, or similar infrastructure-as-code solutions, and exposure to automation of CICD pipelines using Jenkins/Gitlab/Github, etc.

Familiarity with machine learning frameworks (TensorFlow, PyTorch, etc.) and deep learning model deployment pipelines. Exposure to vLLM or NVIDIA software stack for data & model management is preferred.

Expertise in performance optimization tools and techniques for GPUs, including memory management, parallel processing, and hardware acceleration.