Global Brain Corporation

Infrastructure Engineer

• Design and scale Kubernetes- and Terraform-based infrastructure across customer environments.
• Define standards for networking, security, CI/CD, and multi-region deployments.
• Build and maintain metrics, logging, tracing, dashboards, and SLOs.
• Diagnose and improve performance across distributed systems and AI workloads.
• Support high-performance inference, data pipelines, and large-scale backend services.
• Ensure systems scale reliably under fast-growing and unpredictable workloads.
• Partner with technical leaders on architecture and mentor engineers.

AI Platform Engineer, Infrastructure

Business Operations

• You'll build the systems that support our company’s most important operations, from how the company runs day to day to how we go to market. The areas below are representative rather than fixed; expect the mix to shift as the company grows and as more of this work becomes automated.
• Own the businesses internal operations, areas like procurement, onboarding, and vendor management, and the processes that hold them together.
• Build and improve the systems behind our go-to-market, partnering with engineering to make how we sell faster, more consistent, and increasingly automated.
• Set up systems for smoother deal processes, working cross-functionally with privacy, legal, sales, product, and engineering to help us manage deal volume at scale.
• Partner with finance and engineering to build the systems and reporting that tie our go-to-market — pipeline, deals, revenue — to how we plan and forecast the business.
• Replace ad-hoc workarounds with durable, lightweight systems that hold up as the team grows, building the operational plumbing that’s currently informal or missing.
• Identify opportunities to build or improve systems before they become bottlenecks, and think in terms of the infrastructure the company will need as it scales.
• Make company and team goals legible, and support how we focus our efforts — so the team knows what matters and where to spend its energy.

Business Operations Lead

AI Engineer

• Run discovery and engagement operations, leading structured discovery with customers, developing value sizing and product proposals, mapping their needs, and running the day-to-day rhythm of an engagement so nothing falls through the cracks.
• Own end-to-end delivery: own your engagements from kickoff to adoption: lead delivery sprints and deployment timelines, coordinate the cross-functional delivery team, and ensure timely, high-quality, audit-ready solutions in regulated environments.
• Define and prioritize features: partner with clients to identify high-impact problems across their workflows, and define the AI-driven product features that address them.
• Translate business needs: convert customer requirements and operational pain points into clear technical specifications and use cases for internal engineering teams.
• Drive adoption and change management: onboard stakeholders and engage users, lead the people-and-process change required for new ways of working to take hold, and gather direct feedback to drive continuous improvement.
• Drive program success and commercial expansion: partner with the client to deliver measurable program outcomes, and own the commercial agenda within each account, building the trust and impact that expand our relationship over time.
• Support AI diligence and value creation: contribute to deep-dive assessments of target companies’ AI strategies, data infrastructure, and technical maturity, and help shape plans that connect AI capabilities to commercial outcomes.
• Bridge business and technology: translate complex AI/ML capabilities into actionable recommendations for investment committees, portfolio executives, and senior client stakeholders.
• Collaborate cross-functionally: serve as the bridge between customers and internal teams, including data scientists, engineers, and product leads.

AI Deployment Lead – GTM

Insurance

• Annotate insurance documents, review AI-generated outputs, and help develop rubrics and benchmarks that define quality for machine learning pipelines.
• Determine whether the alleged loss triggers coverage, citing specific insuring agreements, exclusions, and conditions.
• Assess the facts of loss and the strength of the liability case against the insured.
• Interpret the stage and trajectory of ongoing claim litigation.
• Annotate claimed amounts and expected loss value based on available claim information.

Insurance SME – Policy & Claims Labeling

Artificial Intelligence

• Own the delivery of your engagements from kickoff to adoption: lead agile sprints and deployment timelines, coordinate the cross-functional delivery team, and ensure timely, high-quality, audit-ready solutions in regulated environments.
• Partner with clients to identify high-impact problems across their workflows and define AI-driven product features to address them.
• Convert customer requirements and operational pain points into clear technical specifications and use cases for internal engineering teams.
• Engage users and onboard stakeholders; lead the people-and-process change required for new ways of working to take hold, and gather direct feedback to drive continuous improvement.
• Partner with the client to deliver measurable program outcomes, and own the commercial agenda within each account, building the trust and impact that expand our relationship over time.
• Contribute to deep-dive assessments of target companies' AI strategies, data infrastructure, and technical maturity. Help shape plans that connect AI capabilities to commercial outcomes.
• Translate complex AI/ML capabilities into actionable recommendations for investment committees, portfolio executives, and senior client stakeholders.
• Serve as the bridge between customers and internal teams including data scientists, engineers, and product leads.

AI Deployment Lead

Sales Engineer

• Travel with GTM teams to agency sites across the US, serving as the technical lead in pursuit of new government business
• Run discovery with agency leaders, program offices, and technical staff to map mission workflows, data landscapes, and constraints — then translate them into actionable solution designs
• Build and deliver compelling demos, proofs of concept, and technical proposals tailored to each agency's real mission problems
• Own the technical narrative through the deal cycle: solution architecture, security and compliance requirements (FedRAMP, ATO processes, data governance), integration scoping, and pilot success criteria
• Support capture and proposal efforts with technical volumes, responses to RFIs/RFPs, and architecture documentation
• Partner with Deployed Engineering to ensure what we sell is what we ship — and that pilots convert to long-term production deployments
• Feed customer insight back into product: what's blocking deals, what's resonating with agencies, and where the next mission opportunities are
• Help build the government sales engineering playbook from the ground up — demo environments, technical collateral, and repeatable POC frameworks

Systems Engineer

• Own the corporate technology layer end-to-end — identity and access management, SaaS tooling, device security, and the employee-facing systems that keep a fast-growing company running safely.
• Own our Okta environment and consolidate SSO: migrations, managing provisioning, lifecycle automation, and federation across all departments.
• Implement and improve our 3-tier access entitlement model: auto-grant for Tier 1, manager approval for Tier 2, manager + security approval for Tier 3.
• Build and operationalize HRIS-triggered provisioning and offboarding automation across our identity and SaaS stack.
• Support access review programs and contribute to SOC 2 compliance efforts — maintaining audit trails, approval chains, and provisioning logs.
• Contribute to Zero Trust and endpoint security initiatives: device trust, EDR integration, MDM, and least-privilege access across corporate and BYOD devices.
• Drive SaaS hygiene across our tooling estate — shadow IT discovery, configuration hardening, and data classification in partnership with the security team.
• Build the integrations and automation that eliminate manual IT work — connecting identity, HRIS, and SaaS tooling into scalable provisioning workflows.
• Own the access request process end-to-end, and maintain the runbooks, documentation, and self-service resources that keep the team unblocked.
• Partner on IT support for a 70+ person team –– triaging and resolving hardware and software issues with pragmatism and good judgment.
• Own device lifecycle and support new hire onboarding end-to-end, ensuring a seamless Day 1 experience.

IT Systems Engineer

Product Marketing

• Build, manage, and close a pipeline of enterprise insurance opportunities
• Develop trusted relationships with senior decision-makers at U.S. carriers, MGAs, reinsurers, and other insurance institutions
• Leverage own network, industry events, and broker referral partners for pipeline generation
• Partner closely with product, engineering, and leadership to translate customer pain points into AI solutions
• Shape Brain Co.’s sales motion for the insurance vertical
• Navigate complex enterprise buying processes across various stakeholders
• Represent Brain Co. credibly in the insurance market.
• Lay the foundation for a future insurance vertical team.

GTM Lead, Insurance

Marketing

• Take our positioning and translate it into the specific narratives, assets, and campaigns that resonate by segment: government, healthcare, regulated industries, and Fortune 500.
• Own demand gen end-to-end. Generate qualified leads and help convert them.
• Build the enablement materials and customer-facing assets that help our GTM team close deals, and iterate quickly based on what’s working in the field.
• Decide which conferences, summits, and industry moments are worth showing up at, then own execution end-to-end.
• Work alongside the CMO, GTM leaders, product, and engineering to keep the story consistent externally and iterate quickly internally.

Marketing Lead

General Counsel

• Advise on offer letters, contractor agreements, equity grants, employee policies, and terminations; coordinate with outside employment specialists as needed.
• Support review and negotiation of term sheets, SAFEs, and equity financing documents alongside company and investors’ counsel.
• Handle commercial lease review and negotiation; manage outside counsel where appropriate.
• Stay ahead of legal and regulatory developments affecting our business, including AI, govtech, data privacy, security, and permitting.
• Serve as legal SME for the teams building Brain Co.’s AI-assisted legal and regulatory tools.

Commercial Counsel

Compliance

• Own the end-to-end GRC program: SOC 2 Type II and HIPAA today, and the path through ISO 27001, NIST 800-171, FedRAMP/GovRAMP, GLBA, and MENA-specific regimes that don’t map cleanly to a US playbook.
• Build the data handling backbone: how customer data is classified, where it lives, who can touch it, and how we prove it - across Azure, on-prem MENA deployments, and the bespoke deployments we run for governments and hospitals.
• Run audits as a builder, not a project manager: Own evidence, controls, gap remediation, and audit response, and automate the evidence pipeline so we’re not rebuilding workpapers every cycle.
• Stand up third-party risk as a real program: vendor reviews, data flow inventory, contractual security obligations, and a reassessment cadence that keeps pace with our SaaS footprint.
• Be the function that unblocks enterprise deals: Build the customer-trust surface — security questionnaires, trust portal, DPAs, BAAs, customer-facing docs — so customers understand how we handle their data before they have to ask.
• Partner with engineering: Bake compliance into the product: control inheritance from Azure, policy-as-code, automated access reviews, audit-ready logging, and evidence collection that runs without a human in the loop.
• Run a single risk operating cadence across HR, Finance, Legal, IT, and Engineering: so data handling, vendor approvals, and audit requests always have a clear owner.
• Be the translator between technical reality and regulatory expectations: the person engineers trust to interpret a control, and the person customers and auditors trust to explain the system behind it.