AI Platform Engineer

One of our largest Telecom customers is seeking a skilled software engineer to join their Data Platform team within the Chief Data Office. This Software 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.

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Strong proficiency in Python, with experience building and maintaining production services.

-Hands-on experience with Docker and Podman for containerization and image management.

-Practical experience with Postgres, including query optimization and schema design.

–Solid working knowledge of Kubernetes, including deployments, services, autoscaling, and

troubleshooting workloads in production.

-Direct experience with vLLM for LLM inference serving.

-Experience deploying models with KServe and experience with Knative for serverless workloads or event-driven scaling.

-Comfortable working in a Linux environment and using standard CLI tooling.

-Cloud platform experience, such as Azure/AKS, AWS/EKS, or GCP/GKE.

-Strong debugging skills across the stack - from application code to infrastructure -Experience with GPU-aware scheduling and resource management in Kubernetes.

-Familiarity with LLM-specific optimizations: speculative decoding, quantization (FP8/INT8), KV cache

management, and continuous batching.

-Experience with API gateways or LLM routing layers, such as LiteLLM or similar.

-Background in SRE practices: SLOs/SLAs, incident response, and on-call tooling.

-Experience with Helm, ArgoCD, or other GitOps deployment tooling.

-Familiarity with observability stacks, including Prometheus, Grafana, and OpenTelemetry.