Founding Platform Engineer (SRE / DevOps / ML Infra)

We are looking for an exceptional Founding Platform Engineer with deep expertise in Site Reliability Engineering (SRE), DevOps, cloud infrastructure, and production ML systems. This is a high-ownership, hands-on engineering role where you will architect, build, and scale the foundational infrastructure powering Valo's AI agents, voice systems, and machine learning workloads.

The ideal candidate has spent at least 5 years in SRE/DevOps or platform engineering and has personally owned, deployed, and maintained at least one business-critical ML system in production. You should bring strong expertise in cloud infrastructure, distributed systems, data engineering, and production reliability, with the ability to make critical architectural decisions independently.

The candidate will have responsibilities across the following functions:

Cloud Infrastructure and Platform Engineering:

• Architect, build, and operate highly scalable, secure, and resilient cloud infrastructure, primarily on AWS.
• Design and manage containerised workloads using Kubernetes, ECS, and Docker.
• Implement Infrastructure as Code using Terraform, AWS CDK, or equivalent tools.
• Build robust CI/CD pipelines, automated deployment workflows, and safe rollback mechanisms.
• Design infrastructure capable of supporting millions of users and large-scale concurrent workloads.

Production ML Infrastructure and Reliability:

• Own the deployment, scalability, availability, and operational reliability of mission-critical machine learning systems.
• Build and maintain infrastructure for production ML inference workloads, including LLMs and speech models.
• Manage GPU-based infrastructure, inference services, model serving, and workload orchestration.
• Optimise model-serving infrastructure for latency, throughput, reliability, and cost efficiency.
• Ensure production ML systems meet strict uptime, monitoring, and recovery requirements.

Data Engineering and Distributed Systems:

• Design and maintain reliable data infrastructure, including databases, message queues, streaming systems, and distributed workers.
• Work with technologies such as PostgreSQL, Redis, Kafka, and other distributed data-processing systems.
• Build scalable data pipelines supporting ML inference, application services, and operational workloads.
• Ensure data consistency, fault tolerance, and efficient processing across distributed systems.
• Collaborate with AI/ML engineers to support data and model infrastructure requirements.

Site Reliability Engineering and Observability:

• Establish end-to-end observability across infrastructure, application services, and ML workloads.
• Implement monitoring, logging, tracing, alerting, and incident-response frameworks.
• Define and manage SLIs, SLOs, and operational reliability standards.
• Own production incident management, root-cause analysis, and post-incident improvements.
• Proactively identify infrastructure bottlenecks and eliminate recurring reliability issues.

Infrastructure Security and Scalability:

• Design secure infrastructure for sensitive user data, credentials, payments, and AI-agent operations.
• Implement IAM, secrets management, network isolation, access controls, and infrastructure hardening.
• Architect isolated execution environments for concurrent AI agents and browser-based workloads.
• Ensure secure, fault-tolerant execution of long-running workflows and critical transactions.

Requirements:

• 5+ years of hands-on experiencein SRE, DevOps, infrastructure engineering, or platform engineering.
• Mandatory: Direct ownership of at least one mission-critical, production-grade ML system, including its deployment, monitoring, scaling, and operational reliability.
• Exceptional understanding of AWS cloud infrastructure, networking, compute, storage, security, and distributed systems.
• Strong hands-on expertise withKubernetes/ECS, Docker, Terraform/CDK, and CI/CD pipelines.
• Strong understanding of data engineering fundamentals, including data pipelines, databases, messaging systems, and distributed processing.
• Experience operating highly available, low-latency production systems at meaningful scale.
• Proven ability to troubleshoot complex infrastructure issues and independently lead production incident resolution.
• Strong automation and scripting capabilities using Python, Bash, or similar languages.
• Ability to independently architect and own infrastructure in a fast-moving startup environment.

Strongly Preferred:

• Experience managing GPU infrastructure and production LLM/ML inference systems.
• Exposure to model-serving technologies such as vLLM, NVIDIA Triton, or equivalent.
• Experience with real-time voice AI, speech processing, or latency-sensitive inference workloads.
• Experience scaling high-concurrency systems, long-running workers, or isolated execution environments.
• Background in high-scale AI/ML product companies, fintech platforms, or infrastructure-intensive SaaS businesses.
• Experience building platform infrastructure from scratch or being an early infrastructure hire.

Skills

AWS, CI / CD, DevOps, Docker, Kubernetes, Platform, Python, SRE, Terraform