Director of Platform Engineering

Stack

Responsibilities

• ▹Own the multi-year technical strategy and roadmap for the Cloud Platform, Productivity Platform and AI Platform teams
• ▹Operate the platforms as products: product ownership, golden paths, a self-service catalog and a backlog driven by internal customer demand
• ▹Define and report the metrics that prove platform value: adoption, DORA metrics, change failure rate, time-to-first-deploy, developer satisfaction and unit cost per workload
• ▹Reduce cognitive load by abstracting infrastructure complexity behind documented interfaces, reusable templates and secure defaults
• ▹Set standards for infrastructure as code, policy as code, service catalogs and platform APIs
• ▹Lead the design, delivery and operation of resilient multi-cloud infrastructure across AWS, Google Cloud and Azure: compute, container orchestration, networking, identity, storage and observability
• ▹Drive Kubernetes and container platform strategy, progressive delivery, service mesh, secrets management and zero-trust network patterns
• ▹Own cloud financial management (FinOps): capacity planning, commitment strategy, showback and chargeback, continuous cost optimization
• ▹Embed guardrails, automated control validation and audit evidence generation into the platform with Security, Risk and Compliance
• ▹Own the developer productivity toolchain: source control, CI/CD, artifact and package management, build and test infrastructure, environment provisioning and developer portal
• ▹Continuously improve build, test and release cycle times and pipeline reliability
• ▹Standardize SDLC tooling including supply chain security, SBOM generation and artifact provenance
• ▹Build and operate the shared AI platform: model gateway and routing, inference and model serving, retrieval and vector data services, feature and embedding pipelines, prompt and artifact versioning, agent orchestration
• ▹Establish MLOps and LLMOps practice: reproducible training and fine-tuning, automated evaluation and regression testing, tracing, drift and quality monitoring, safe rollback
• ▹Manage GPU and accelerator capacity, quota and token consumption across clouds with transparent cost attribution
• ▹Operationalize responsible AI governance with Security, Legal and Privacy: data residency, access and tenancy isolation, output safeguards, auditable model usage
• ▹Own production health of the platform estate: 24x7 support, incident command, problem management, blameless postmortems
• ▹Define and defend SLOs, SLIs and error budgets
• ▹Lead resilience engineering: capacity and failure-mode analysis, disaster recovery, chaos and game day exercises
• ▹Participate in and lead escalation for a 24x7 on-call rotation
• ▹Lead and develop a multi-team organization of engineering managers, staff and principal engineers and platform engineers, including hiring, performance management and succession planning
• ▹Build a culture of engineering excellence, operational ownership and knowledge sharing
• ▹Translate technical strategy and risk into clear insight for executives and non-technical stakeholders
• ▹Manage budget, vendor relationships and contract negotiation for platform and AI infrastructure spend
• ▹Build consensus and drive change across organizational boundaries

Requirements

• ▹Bachelor's degree in Computer Science, Engineering, Management Information Systems or equivalent practical experience
• ▹Minimum 12 years of experience in software engineering, infrastructure or technology operations
• ▹Minimum 6 years leading engineering teams, including at least 3 years leading managers or multiple teams
• ▹Experience building, operating and scaling production cloud infrastructure in AWS, Google Cloud and Azure in a medium or large enterprise; depth in one and working fluency in the other two
• ▹Proven track record building an internal developer platform or shared platform capability adopted at scale, with measurable improvements in delivery velocity, reliability or cost
• ▹Deep hands-on platform engineering practice: infrastructure as code, Kubernetes and container platforms, CI/CD at scale, observability, policy as code, self-service developer tooling
• ▹Working knowledge of AI and machine learning infrastructure: model serving and inference, RAG, vector data stores, accelerator capacity management, evaluation frameworks, AI governance and safety controls
• ▹Experience owning a 24x7 production environment, including incident command, SLO and error budget management
• ▹Experience with cloud financial management and multi-million-dollar infrastructure budget ownership
• ▹Strong executive communication skills, consensus building and influence across organizational boundaries
• ▹Willingness to participate in and lead escalation for a 24x7 on-call rotation

Nice to have

• ▹Advanced degree in a technical discipline
• ▹Experience standing up an AI or machine learning platform from early stage through broad enterprise adoption
• ▹Experience with agentic systems, tool and context protocols and LLM application observability in production
• ▹Background in cybersecurity, financial services or another regulated or real-time transaction processing industry
• ▹Experience under formal compliance regimes such as SOC 2, PCI DSS, ISO 27001, FedRAMP or GDPR
• ▹Experience leading distributed or globally dispersed engineering organizations

Soft skills

What we offer

• ▹Flexible working options, time off and well-being programs
• ▹Competitive pay and benefits

About the company

Gen is a global company behind consumer brands including Norton, Avast, LifeLock and MoneyLion, delivering cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.