Lead a seven-person Infrastructure and DevOps team supporting GPU-intensive AI model training and inference. Oversee multi-cloud infrastructure, primarily GCP, Kubernetes, Docker, Terraform or OpenTofu, high-performance storage, FinOps, capacity planning, reliability, incident response, and security compliance. Partner with engineering, research, and executive stakeholders while providing architectural guidance and technical escalation for complex infrastructure challenges.
Brahma AI operates at the intersection of enterprise Media Asset Management (MAM) and cutting-edge generative media. We build and scale industry-leading generative AI models, including hyper-realistic digital humans (ATMAN) and multilingual voice synthesis (VAANI), for world-class enterprise clients in entertainment, sports, healthcare, and retail.
Role Overview
We are looking for a Lead Infrastructure / DevOps Engineer to lead our core AI Platform & Infrastructure team. Reporting directly to the VP of Engineering, you will guide a team of 7 engineers responsible for powering our high-performance GPU infrastructure, multi-cloud setup (with GCP as our primary provider), ML model training pipelines, and containerised orchestration environments.
This role balances technical leadership, team management, cloud resource optimisation, and high-level architectural oversight. You will ensure our research and engineering teams have the fast, scalable, and reliable compute environments necessary to train and deploy state-of-the-art AI models.
Key Responsibilities
1. People, Team & Process Leadership (50%)
- Team Management: Lead, mentor, and grow a team of 7 DevOps and Infrastructure engineers through regular 1:1s, performance reviews, and career pathing.
- Sprint & Operational Delivery: Drive agile delivery, sprint planning, and backlog prioritisation to align infrastructure deliverables with AI research and product roadmaps.
- Engineering Standards: Establish best practices for Reliability Engineering, Infrastructure-as-Code (IaC), continuous integration, and incident post-mortems.
- Cross-Functional Alignment: Act as the primary technical bridge between infrastructure, ML researchers, AI application developers, and the VP of Engineering.
2. Resource Management & FinOps (25%)
- GPU & Multi-Cloud Management: Manage high-density GPU clusters across a multi-cloud ecosystem (primarily GCP) optimised for large custom AI model training and real-time inference workflows.
- FinOps & Cost Control: Oversee infrastructure consumption, track cloud/hardware costs, negotiate vendor terms, and optimise GPU utilisation to maintain cost efficiency.
- High-Performance Storage: Oversee high-throughput storage and caching solutions engineered for ultra-fast data retrieval and low-latency access.
3. Architecture, Engineering & Compliance (25%)
- Technical Escalation & Hands-On Oversight: Serve as the senior technical escalation point for complex infrastructure incidents and architecture decisions.
- Automation & IaC: Standardise platform deployments using Infrastructure as Code (e.g., Terraform/OpenTofu) and modern container orchestration (Kubernetes).
- Security & Compliance Collaboration: Partner with security stakeholders to ensure our AI training environments meet industry security standards (e.g., MPA Best Practices, ISO 27001, SOC 2).
Must Haves
- Leadership Experience: Proven track record leading or managing a team of 5+ infrastructure, platform, or DevOps engineers.
- AI/GPU Infrastructure: Hands-on experience architecting and managing GPU-intensive workloads (NVIDIA clusters, cloud AI accelerators) for compute-heavy applications.
- Multi-Cloud & Orchestration: Expertise with Kubernetes, Docker, Terraform (or OpenTofu), and multi-cloud environments (with strong hands-on GCP experience).
- High-Performance Storage: Demonstrated experience designing, optimising, and maintaining high-performance storage architectures and caching layers for demanding compute workloads.
- Cloud & Resource Management: Strong experience with cloud cost governance (FinOps), capacity planning, and vendor interaction.
- Communication: Exceptional stakeholder management skills with the ability to bridge business requirements and deep technical infrastructure details.
Nice to Have
- Experience managing physical data centres, co-location facilities, or hybrid infrastructure environments.
- Working knowledge of ML orchestration frameworks (e.g., Ray, Slurm, Kubeflow).
- Background in media pipelines, VFX tooling, or media compliance standards (MPA, ISO 27001).
- Prior experience working in a hybrid startup/scale-up environment.
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