Jobs at Blackpoint Cyber

Leads the strategy, roadmap, architecture, reliability, and operations of a cloud-based security data platform. Oversees data pipelines, APIs, identity, tenant management, developer tooling, CI/CD, governance, security, and scalability. Partners with Product and SRE, drives operational excellence and AI adoption, and builds a high-performing organization through hiring, mentorship, and technical leadership.

Own and rebuild Blackpoint Cyber’s customer-facing knowledge base, including approximately 150 article rewrites and 100 new articles. Define documentation standards, information architecture, publishing governance, and AI-ready content structures. Collaborate with technical SMEs, validate procedures against products, use support analytics to prioritize content, and align documentation with product releases. Ensure accurate, retrieval-friendly documentation for internal teams, customers, MSP partners, and AI-assisted support workflows.

Provides expert technical support for complex software issues involving microservices, APIs, integrations, operating systems, cloud platforms, and networking. Resolves escalated cases, collaborates with Engineering and Product, analyzes ticket trends, maintains documentation, and mentors junior engineers. Builds and optimizes Zendesk workflows and support automation using tools such as Superblocks, n8n, Tines, and webhooks to improve operational efficiency and customer satisfaction.

Conduct advanced threat hunting and intrusion analysis in a 24/7 SOC environment. Investigate endpoint and network events, support incident response, analyze threats across customer environments, produce incident reports, and serve as an escalation point for complex cyber incidents. The role also develops SOC processes, builds research lab environments, evaluates sandbox technologies, and communicates technical findings to technical and non-technical audiences.

Senior Full Stack Software Engineer responsible for building and owning scalable cybersecurity product experiences across front-end and back-end systems. The role uses TypeScript, Golang, React, Node.js, Kubernetes, cloud platforms, databases, APIs, and CI/CD pipelines. Responsibilities include architecture, performance optimization, security, mentoring engineers, leading projects, Agile planning, and adopting AI-assisted development tools.

Designs and maintains scalable cloud and on-premise infrastructure, Infrastructure as Code, Kubernetes environments, CI/CD pipelines, data streaming, caching, observability, and incident response systems. The role owns AWS reliability and cost efficiency, supports progressive deployments, troubleshoots complex production issues, partners with software teams, and drives automation and continuous improvement across platform operations.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.

Drive technology strategy and architecture across the product portfolio, delivering scalable, secure cloud-native SaaS and endpoint solutions. Champion AI-driven approaches, lead design reviews and PoCs, provide technical leadership, collaborate with product and business stakeholders, and support production systems including on-call troubleshooting and root cause analysis.

Lead global SRE team to design, operate, and optimize scalable cloud infrastructure (AWS/Azure/GCP). Own reliability, observability, incident response, IaC, CI/CD, cost optimization (FinOps), security hygiene, and mentor engineers while contributing hands-on and researching AI-driven SRE tooling.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.