Gabriel Amram

Target professional categories

Professional summary

Technology, product and people - not as separate domains, but as a single discipline. Gabriel Amram is a builder: of products, platforms, organizations and the leaders who run them - grounded in technical depth, shaped by product thinking and connected to business outcomes.

More than 20 years of experience across startup founding and enterprise engineering management, product strategy, platform engineering, distributed systems, data and applied AI. He has operated as a CTO and co-founder, VP R&D and co-founder, and Engineering Group Lead in a large public technology company.

With a track record of identifying technology inflection points early and moving production systems into new paradigms before they become established practice.

Core competencies

Technology

Gabriel's technical focus is on the systems that make other systems work: distributed architectures, platform engineering, service design and AI-native development. He approaches technical problems with a product lens - asking who the customers are, what they need, and how to measure success.

Keywords: distributed systems; service architecture; platform engineering; application infrastructure; developer platforms; authorization and access control; graph-based authorization; monolith decomposition; microservices; sidecar architectures; language-agnostic infrastructure; context propagation; resilience engineering; cloud infrastructure; data pipelines; NLP; LLM-based systems; AI agents; agentic software development.

Leadership

Gabriel develops people and organizations deliberately - growing technical leaders, restructuring teams as strategic levers, and building cultures of ownership and autonomy. He operates as a manager of managers and treats organizational design as a tool, not a constraint.

Keywords: engineering organization leadership; management of managers; technical leadership development; mentoring; career development; organizational restructuring; team design; cross-site leadership; cross-functional leadership; strategy formation; roadmap steering; stakeholder alignment; executive communication; product and engineering partnership.

Business and product

Gabriel connects engineering work to business outcomes rather than treating them as separate concerns. He approaches platform and product decisions with a clear view of customers, adoption, value and cost - and partners closely with product, commercial and executive stakeholders.

Keywords: product strategy; platform-as-a-product; customer-oriented engineering; internal platform adoption; business and technical alignment; cost optimization; operational resilience; engineering velocity; product performance; organizational scaling.

monday.com

Role: Engineering Group Lead
Dates: 2024–Present
Domains: Customer-facing products, platform engineering, service architecture, authorization, developer experience and AI-native engineering
Locations managed: Tel Aviv and London

Gabriel joined monday.com after more than a decade as a startup founder and technology executive. He led two substantially different engineering groups: Ecosystem Products and Server Foundation. Across both mandates he combined technical depth, product orientation, organizational leadership and AI strategy.

Server Foundation, Core Engineering

Dates: 2026–Present

Mandate

Lead foundational engineering capabilities supporting service development and product engineering across monday.com. Primary areas include application-level infrastructure, async communications infrastructure, service architecture, authorization infrastructure, internal developer platforms, service resilience, platform autonomy, engineering velocity and infrastructure efficiency.

Platform-as-product approach

Gabriel reoriented Server Foundation to operate as a product organization rather than a collection of infrastructure teams. Its internal customers are service owners and product engineering teams. Product outcomes include reducing cognitive load, improving developer experience, increasing engineering velocity, preventing incidents, simplifying infrastructure adoption, reducing organization-wide migration work, increasing platform autonomy and controlling cost.

mondayCore

Type: Internal developer platform and AI-ready service architecture
Leadership contribution: Initiated, framed, architecturally steered and led organizationally by Gabriel

Problem

Service teams were responsible for both business logic and a broad set of infrastructure and non-functional concerns, including infrastructure communication, context propagation, retry policies, circuit breakers, rate limiting, cache policies, resource access, deployment topology, platform policy and vendor-specific integration.

This model created duplicated implementation, inconsistent service behavior, increased cognitive load, avoidable incidents, slow platform-wide migrations, dependency on service-team roadmaps, overprovisioning, difficult vendor changes and poor suitability for AI coding agents.

Strategy

Create an AI-ready service-development model in which service owners and coding agents primarily implement business logic and declare operational intent. The platform enforces and implements the relevant infrastructure behavior.

Architecture

• A thin SDK consumed by services.
• A stable service-facing API.
• A language-agnostic sidecar.
• Platform-owned infrastructure components.
• Declarative configuration and intent.
• Centralized enforcement of non-functional requirements.

The sidecar and platform components handle ingress and egress communication, resource communication, context propagation, deployment-topology awareness, retry behavior, circuit breaking, rate limiting, cache-policy and TTL enforcement, platform policies, infrastructure abstraction and vendor-specific implementation.

Value proposition

• Resilience: consistent platform-level enforcement of safeguards that can prevent avoidable incidents.
• Engineering velocity: service teams spend more time on business behavior and less on infrastructure integration.
• AI readiness: coding agents operate against constrained, declarative interfaces.
• Platform autonomy: platform-owned components can evolve without coordinated changes across all service teams.
• Reduced migration cost: company-wide adoption campaigns can be replaced by implementation changes behind stable interfaces.
• Infrastructure efficiency: topology controls support workload-specific deployment models and reduce overprovisioning.
• Vendor flexibility: underlying implementations can change without service owners changing business code.

Gabriel’s contribution

• Identified the architectural and organizational problem.
• Connected technical fragmentation to incidents, velocity, autonomy and cost.
• Defined the platform-as-product framing.
• Initiated the mondayCore program and steered its architecture.
• Led the responsible organization and aligned the initiative with AI-native engineering.
• Connected the initiative to company-level outcomes and worked across product and engineering stakeholders.

Authorization modernization

Transformation: relational authorization model toward a graph-based model.

Problem and strategy

The prior relational approach created challenges around query performance, scalability, change risk, operational resilience and representing complex relationships and permissions. The direction was to move toward a graph-oriented model capable of representing relationships naturally and supporting higher-scale authorization evaluation.

Intended outcomes

Faster authorization evaluation, better scalability, stronger resilience, lower change risk, clearer modeling of complex permissions and better support for future product requirements.

Gabriel’s contribution

Led the responsible group, helped set the architectural direction, connected the infrastructure change to product and company needs, balanced long-term architecture with immediate delivery requirements and worked across engineering and product stakeholders.

Async communications infrastructure

Led the team responsible for monday.com's async communications platform - covering Kafka-based messaging, the reporter service and async communication patterns used across the organization. Ensured reliability, scalability and clear ownership of event-driven infrastructure supporting product engineering teams.

Organizational leadership in Server Foundation

• Led multiple foundational engineering areas and developed technical leaders.
• Aligned team ownership with strategic technical missions.
• Treated engineering structure as a design variable rather than a fixed constraint.
• Worked closely with product leadership inside Core Engineering.
• Prioritized internal-user value and platform adoption.
• Promoted increased use of AI throughout the software-development lifecycle.

Ecosystem Products

Dates: 2024–2026
Products: WorkForms, WorkDocs and Knowledge
Locations: Tel Aviv and London

Mandate

Lead engineering for products embedded within monday.com’s primary customer experience while substantial enough to require independent product, technology and organizational strategy. Responsibilities included engineering strategy, product and technology direction, architecture, delivery, reliability, performance, team building, leadership development, cross-site management, AI adoption and organizational design.

WorkForms modernization

Initial state

WorkForms depended heavily on monday.com’s monolith and shared infrastructure, resulting in tight coupling, limited isolation, scalability concerns, risk to the wider platform during traffic spikes and difficulty scaling campaign-driven workloads independently.

Strategy

Gabriel defined the modernization strategy around three pillars:

• Platform independence: WorkForms should stay operational when monday experiences issues, and a forms traffic spike should never threaten monday's stability.
• Feature competitiveness: close the gap with dedicated forms products on core customer requests, so users have no reason to leave monday's ecosystem for a competing tool.
• Ecosystem cohesion: deepen WorkForms' integration with monday's main products to increase stickiness and coherence across the platform.

The execution challenge was moving on this strategy while customer feature requests kept arriving. Gabriel's approach was to tie migration milestones to feature delivery rather than separating them - each architectural improvement created the headroom for the next feature investment.

Work led

• Separated critical WorkForms flows from the monolith.
• Redesigned architecture for higher traffic, stronger isolation and improved resilience.
• Improved observability, service ownership and deployment independence.
• Steered product and technology direction toward AI-native capabilities.

Outcomes

Greater resilience, higher supported scale, reduced platform risk, improved isolation, faster and safer deployments, better operational visibility and a stronger foundation for future AI capabilities. Drove meaningful growth in active adoption among large enterprise accounts - the primary strategic segment for WorkForms.

WorkDocs and Knowledge

• Improved product performance by several-fold, contributing to increased active adoption across pro and enterprise tiers - driven by expanded feature coverage, greater scale and improved product trust.
• Built and developed the engineering team.
• Strengthened technical ownership.
• Mentored engineering managers, technical leads and senior engineers.
• Pushed both products toward AI-native capabilities - establishing agentic practices in planning, implementation, review and operation.
• Worked closely with product partners on strategy and priorities.

AI-native leadership

Gabriel drove adoption of AI throughout the engineering lifecycle, including planning, technical design, implementation, background coding agents, code review, automated testing, quality assurance, accessibility testing, architecture decisions, monitoring, incident investigation and engineering context. He emphasized designing systems, APIs, rules and organizational practices that allow agents to operate safely and effectively rather than merely adding conversational tools to existing workflows.

People and organizational leadership

• Led engineering teams across Tel Aviv and London.
• Built and restructured teams.
• Mentored engineering managers, technical leads and senior engineers.
• Developed new technical leaders and created clearer ownership.
• Adjusted team structure as company and product priorities changed.
• Balanced product investment, customer obligations, maintenance and strategic focus.
• Worked across product, design, analytics, customer-facing teams and senior leadership.

Brew

Role: CTO and Co-Founder
Dates: 2019–2023
Domain: AI-powered marketing strategy and intelligence
Funding: $12M seed round from Aleph, Mizmaa and Gefen
Organization: 50+ people total; R&D organization of ~25 engineers, data scientists, architects and technical leaders
Outcome: Grew a portfolio of high-value enterprise customers.

Responsibilities included company technology strategy, engineering organization leadership, data-science leadership, software architecture, cloud infrastructure, product strategy, leadership development, recruitment, delivery, quality and business alignment.

• Built and led a multidisciplinary R&D organization and developed managers and technical leaders.
• Created growth plans and engineering practices.
• Led the organization through funding rounds, product launches and market transitions.
• Built large-scale data pipelines, audience and topic analysis, NLP systems, search and analytics, and an LLM-based marketing strategy agent.

Platform architecture

Brew's platform was built around three core components:

• Crawlers and sourcers: discovered and parsed company marketing activities at scale - events, conferences, webinars, blogs, press releases, podcasts and social content. Extracted structured intelligence: companies, topics, traction and engagement, target audiences (distinct from audiences mentioned in the text), verticals and industries.
• Contextual graph: stored all extracted intelligence in a graph model enabling non-obvious competitive insights. For example: two companies not typically seen as competitors could be identified as competing for the same target audience, revealing competitive dynamics invisible to conventional analysis.
• Marketing strategy layer: the customer-facing component, allowing users to ask strategic questions - such as how to penetrate a new market, who is active there, what share of voice they hold, and what differentiation strategy would be most effective. Initially built as structured dashboards; redesigned around LLMs in late 2022 to support open-ended natural language strategy queries.

Marketing strategy agent (2022)

In late 2022, when ChatGPT was released, Gabriel spearheaded a strategic pivot to rebuild the strategy layer as an LLM-based expert agent. While early tooling such as LangChain was emerging, it was not yet production-ready and carried significant survival risk - the decision was made to build the orchestration architecture in-house rather than take a dependency on unproven frameworks.

Architecture

• An orchestrator agent that decomposed user queries and routed to specialized sub-agents.
• Specialized sub-agents: an expert SQL executor, a graph and chart renderer, an analysis and reasoning agent and others.
• Multi-tenant data isolation enforced at the agent level - no data leakage across customer boundaries.
• Domain constraints to prevent out-of-scope responses.
• Caching of answers and chains-of-thought for cost optimization.

Organizational dimension

Spearheading this shift required changing the mental model of company leadership and all of R&D. At the time, LLM-native product development was not yet an established pattern. Gabriel led the case for this architectural direction and drove the organizational change needed to execute it - making Brew an early mover in production agentic systems.

Technologies: Python, Django, FastAPI, AWS, PostgreSQL, Elasticsearch, Redis, Terraform and Vue.js.

EverThere, formerly Zoliro

Role: VP R&D and Co-Founder
Dates: 2012–2019
Domain: Conference, audience and advertising technology

• Co-founded the company and built the initial product and architecture.
• Led engineering and R&D.
• The company's IP was subsequently acquired by one of its customers. Developed an AI-powered algorithm matching conferences with relevant advertisers - at a time when AI-powered matching was far from standard practice. Grew to hundreds of customers with strong recurring engagement.
• Built systems for scalability, performance and data integrity.
• Worked with the CEO, product, sales and customer-success functions.
• Built and led cross-functional engineering teams.

Israel Defense Forces - MAMRAM

CTO and Founding Team Member, Cyber Defense Unit · 2011

Helped establish a new cyber-defense technology unit, led early technical foundations and contributed to technical and organizational direction.

Database Team Lead, HR Technology · 2009–2011

Led database engineering for a large-scale organizational HR platform.

Programming Instructor, Course Lead and Project Manager · 2005–2009

Taught software development, led programming courses, managed instructors and curriculum, managed software projects and developed future engineering talent. Captain, reserve service.

Education

B.Sc. Biotechnology · The Open University of Israel

Software Engineering and Programming Program · MAMRAM, Israel Defense Forces

Languages

Hebrew: Native · English: Fluent · French: Fluent

Professional writing

Gabriel writes about engineering leadership, CTO responsibilities, organizational design, autonomy and empowerment, career development, platform engineering, technical strategy, AI-native engineering and the relationship between product, technology and organizations.

Writing archive: amram.co/stories