Platform Engineer

Required Skills & Experience
- Strong hands-on python coding & development experience as well as experience working with FastAPI \n-Expertise with Docker & Kubernetes, including deployments, services, autoscaling, and \ntroubleshooting workloads in production.\n-Practical experience with Postgres, including query optimization and schema design.\n-Cloud platform experience, such as Azure/AKS, AWS/EKS, or GCP/GKE.\n-Strong debugging skills across the stack — from application code to infrastructure

Nice to Have Skills & Experience
- Direct experience with vLLM or LLM inferencing \n-Experience with GPU-aware scheduling and resource management in Kubernetes.\n-Familiarity with LLM-specific optimizations: speculative decoding, quantization (FP8/INT8), KV cache \nmanagement, and continuous batching.\n-Experience with API gateways or LLM routing layers, such as LiteLLM or similar.\n-Background in SRE practices: SLOs/SLAs, incident response, and on-call tooling.\n-Experience with Helm, ArgoCD, or other GitOps deployment tooling.\n-Familiarity with observability stacks, including Prometheus, Grafana, and OpenTelemetry.

Job Description

One of our largest Telecom customers is seeking a skilled DevOps Engineer to join their Data Platform team within the Chief Data Office. This Engineer will play a key role in building, operating, and scaling a production-grade AI inferencing platform that supports high-throughput, low-latency large language model (LLM) workloads. The position combines software engineering, platform engineering, and reliability engineering responsibilities, with a focus on developing Python-based services, automating model deployments, and optimizing model serving performance using vLLM, Kubernetes, KServe, and Knative. The engineer will be responsible for containerization with Docker and Podman, maintaining and tuning PostgreSQL data models, troubleshooting production issues, and driving platform reliability through observability and automation. Working closely with ML engineers and platform architects, this individual will help scale mission-critical AI services, support new model rollouts, and continuously improve the infrastructure that powers enterprise AI applications.