SDE II - AI Platform
Safe Security (company site, opens in a new tab)2–4 yrsBengaluruOn-siteDirect career pageEst. 20–35 LPA
Posted on 18 Sept
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Job description
Structured overview of role & requirements
About This Role
- Own and build the AI platform enabling AI/ML engineers to deploy, version, evaluate, and monitor workloads with self-service APIs and workflows.
- Manage GPU infrastructure including cluster design, provisioning, scheduling, isolation, and utilisation optimization for multi-tenant enterprise environments.
- Run and optimize large-scale LLM inference production workloads meeting latency, throughput, and cost targets while ensuring secure, multi-tenant, observable platform operations.
Minimum Requirements
- 2-4 years experience building and operating production software and infrastructure with end-to-end ownership.
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
- Strong programming skills in Python and/or Go; deep experience with Kubernetes, Docker, Linux, networking, and distributed systems.
- Hands-on experience with GPU cluster operations, NVIDIA CUDA ecosystem, GPU scheduling and troubleshooting, and production LLM inference serving using tools like vLLM, Triton, or KServe.
Ideal Candidate Profile
- Background in platform engineering, infrastructure, SRE, distributed systems, or MLOps with a focus on AI workloads and GPU infrastructure.
- Proven ownership of secure, scalable, multi-tenant SaaS services and backend APIs in cloud environments (AWS/GCP/Azure) using Terraform-driven infrastructure as code.
- Experienced in building observability and automated CI/CD pipelines driving measurable improvements in GPU utilisation, inference cost, and model evaluation quality.