Head of Infrastructure & Platform Engineering

About Disseqt AI

Disseqt is the Assurance Layer for Enterprise AI Operations. One platform covers the full deployment lifecycle: pre-production testing, runtime policy enforcement, continuous post-deployment monitoring and compliance reporting.

AI agents are non-deterministic. They hallucinate. They produce unexpected outputs. They behave differently in production than in any test environment. They drift. They degrade. Enterprises deploying AI agents at scale have limited visibility into what those systems are doing after go-live. Legacy GRC tools were built for predictable software and cannot monitor what changes continuously.

Disseqt closes that gap. With regulations like the EU AI Act and ISO 42001 now mandating full lifecycle AI governance, enterprises using Disseqt are winning regulated deals, faster and with less risk.

The Role

You'll own everything that keeps Disseqt running, scaling, and shipping fast. Infrastructure, platform tooling, delivery pipelines, ML infra, data infrastructure, cloud costs and the ability to scale all of it as we grow. This is a player-coach role: you'll lead a lean team but stay deeply hands-on.

What You'll Own

• Cloud Infrastructure - Design, build, and operate our cloud environment (AWS/GCP). Reliability, scalability, and cost efficiency are your north star
• ML & AI Infrastructure - Model serving, inference optimization, vector databases, GPU resource management, and MLOps pipelines
• Data Infrastructure - Own the data platform layer: pipelines, storage, lakehouse architecture, real-time streaming, and data reliability. Ensure data flows cleanly across our AI and product stack
• Platform Engineering - Internal developer platform, Kubernetes, CI/CD tooling, and developer experience
• Delivery Pipelines - Enterprise-grade release pipelines, deployment automation, and environment management
• DevSecOps - Security embedded into every pipeline and infrastructure layer not bolted on
• Scale & Reliability - Design for scale from day one. SLO definitions, SRE practices, incident response, and the ability to go from startup to enterprise-grade without rebuilding everything
• Cost Engineering - Cloud spend ownership, FinOps practices, resource optimization

What We're Looking For

• 10+ years in infrastructure, platform, or DevOps engineering - with at least 2–3 years in a leadership role
• Deep hands-on experience with Kubernetes, Terraform, and major cloud platforms (AWS/GCP)
• Built or operated ML infrastructure - model serving, MLOps pipelines, GPU infra
• Experience building and operating data infrastructure at scale - pipelines, streaming (Kafka/Flink), data lakes, or warehouse tooling
• Strong CI/CD and delivery pipeline experience at scale
• Proven track record of scaling systems - you've taken infrastructure from early-stage to high-throughput production
• Security-first mindset - DevSecOps is second nature
• Cloud cost management - you've owned a budget and optimized it
• Startup experience preferred - comfortable with ambiguity and building from scratch
• Excellent communicator - can translate infra complexity to product and business stakeholders

Nice to Have

• Experience with AI governance, compliance, or regulated enterprise environments
• Familiarity with LLMOps and agentic AI deployment patterns
• FinOps certification or equivalent hands-on cost engineering experience

Why Disseqt

• Greenfield infra - build it right from the start
• AI-era problems - GPU clusters, inference pipelines, agent deployment, data at scale
• Lean team, high ownership
• Backed by a mission: Ship Responsible AI