Senior System Engineer

Flam is building AI Infrastructure for Brands in Immersive Advertising spanning across all channels viz. Digital, Broadcast TV, Retail, Communications, Print, OOH etc.

Vision : The Immersive & Interactive Layer for Every Screen & Surface

Flam aims to redefine how consumers interact with ads, content in every shape and form, retail aisles, live broadcasts and fan moments turning content and interfaces into shoppable, shareable experiences that deliver measurable ROI. Flam has raised a $14 million Series A round led by global technology investor RTP Global with participation from Dovetail and select others bringing the total funding to $22 million.

The next phase of growth is to accelerate R&D on its app-less GenAI infrastructure that lets brands create, publish and measure high-fidelity MR, 3D & Digital experiences in

Key Focus Areas :

Product Roadmap : Upcoming releases include GenAI-driven 3D asset generation, Democratising MR deployment at scale, Enterprise Suite of Products across Industries, and Infrastructure for broadcasters and fan engagement.

Geography : Funds will support new enterprise pods in North America, Europe and the Middle East while deepening Asia operations.

Partnerships : Flam will expand its partner program for creative studios and global platforms, enabling Fortune 500 brands to move from pilot to rapid global roll-out.

What You'll Be Doing :

Design and implement custom Kubernetes controllers that manage GPU node lifecycle across multiple cloud providers, handling provisioning, bootstrapping, and de provisioning with zero manual intervention

Build and maintain autoscaling systems that analyze pod resource requirements (GPU count, memory, CPU) and automatically provision appropriate instance types from provider APIs

Develop microVM-based isolation for multi-tenant GPU workloads using cloud-hypervisor with VFIO GPU passthrough, enabling secure, high-performance compute for inference and training workloads

Create node bootstrapping automation that provisions bare-metal or cloud instances with container runtimes, GPU drivers, Kubernetes components, and custom networking configurations via cloud-init, Ansible, or Terraform

Implement sophisticated networking solutions connecting hybrid infrastructure - GKE / GCE control planes with external GPU workers using WireGuard, custom CNI configurations, and cross-cloud service mesh

Build Kubernetes operators and CRDs for managing ML infrastructure components like model registries, inference endpoints, training job orchestration, and GPU time-slicing configurations

Design monitoring, cost optimization, and capacity planning systems that provide visibility into GPU utilization, workload patterns, and infrastructure efficiency across heterogeneous compute pools.

Work closely with ML researchers to understand workload requirements and translate them into infrastructure automation that enables rapid experimentation and production deployment.

What We Need to See :

5+ years of experience building production Kubernetes systems with deep expertise in controllers, operators, CustomResourceDefinitions, and API machinery

Strong proficiency in Go and experience building scalable, reliable services that manage complex distributed systems

Hands-on experience with GPU infrastructure in Kubernetes - NVIDIA GPU Operator, device plugins, time-slicing configurations, or custom GPU scheduling logic

Deep understanding of Kubernetes architecture including admission controllers, scheduler extenders, resource lifecycle management, and cluster autoscaling mechanisms

Demonstrated ability to design and implement automation systems that replace manual processes with API-driven, self-service tooling

Experience with at least one cloud provider's APIs (GCP, AWS, Azure) for programmatic compute provisioning and management

Strong Linux systems knowledge including networking (iptables, WireGuard, CNIs), storage (LVM, device mapper), and virtualization (KVM, QEMU, cloud-hypervisor)

Bachelor's / Master's degree in Computer Science, Engineering, or equivalent practical experience

Ways to Stand Out from the Crowd :

Experience building hybrid / multi-cloud Kubernetes architectures with cross-provider networking and unified control planes

Deep familiarity with microVM technologies (Firecracker, cloud-hypervisor, Kata Containers) and their application to GPU workloads

Hands-on experience with VFIO GPU passthrough, SR-IOV, MIG (Multi-Instance GPU), or other GPU virtualization technologies

Track record of building custom cloud controllers or provider implementations for bare-metal or specialized compute

Experience with ML infrastructure patterns - model serving, training orchestration, experiment tracking, or distributed training frameworks

Contributions to upstream Kubernetes projects, CNCF ecosystem tools, or GPU-related open source projects

Understanding of cost optimization strategies for GPU compute, including spot instances, preemption handling, and intelligent workload placement

Experience with Infrastructure as Code (Terraform, Pulumi) for complex multi-provider deployments