We are seeking a candidate who views AI tooling as a fundamental force multiplier in their daily engineering process. This position is central to our transition into an AI-native function, requiring an individual capable of making decisive, pragmatic architectural choices on reversible matters to maintain momentum. We need an experienced builder of production-grade, data-centric systems who is obsessed with delivering customer value and possesses a deep, curious enthusiasm for the transformative potential of AI.
What You Will Build:
AI-Native Systems Development. Design, build, and own scalable data and ML pipelines, backend services, and AI-powered capabilities that are part of the platform's production decision-making layer. AI and ML components are runtime dependencies in this role – not research projects or experiments. Candidates will have strong back end and data engineering skills to thrive in this space.
Daily Shipping. Decompose complex work into safely mergeable increments and ship them daily. Treat large, multi-day pull requests as a risk to momentum. Use feature flags, canary releases, and rollback architecture to manage risk through isolation – not through avoidance.
AI-Augmented Engineering Workflow. Leverage AI-assisted development tooling (code generation, automated testing, architecture prototyping) as a core workflow multiplier. Evaluate and experiment with emerging AI tools and frameworks with direct hands-on engagement. Bring technical depth to AI fluency – architecture and capability tradeoffs, not surface-level awareness.
End-to-End Ownership. Own your work from design through production deployment, operational monitoring, and business impact measurement. Accountability extends beyond the feature to CI/CD pipeline health, observability, cost efficiency, and domain-level outcomes.
Architectural Decision-Making. Make pragmatic, timely architectural choices that balance modern AI and data technologies with reliability, cost, and delivery speed. Distinguish reversible vs. irreversible decisions and move forward without waiting for consensus on the former. Document decisions in lightweight ADRs and own the outcomes.
Cross-Functional Collaboration. Partner with product, design, infrastructure, and GTM teams to translate customer and business needs into technical solutions. Operate with business awareness – understand how your systems impact revenue, customer outcomes, and strategic priorities.
What You Will Build:
AI-Native Systems Development. Design, build, and own scalable data and ML pipelines, backend services, and AI-powered capabilities that are part of the platform's production decision-making layer. AI and ML components are runtime dependencies in this role – not research projects or experiments. Candidates will have strong back end and data engineering skills to thrive in this space.
Daily Shipping. Decompose complex work into safely mergeable increments and ship them daily. Treat large, multi-day pull requests as a risk to momentum. Use feature flags, canary releases, and rollback architecture to manage risk through isolation – not through avoidance.
AI-Augmented Engineering Workflow. Leverage AI-assisted development tooling (code generation, automated testing, architecture prototyping) as a core workflow multiplier. Evaluate and experiment with emerging AI tools and frameworks with direct hands-on engagement. Bring technical depth to AI fluency – architecture and capability tradeoffs, not surface-level awareness.
End-to-End Ownership. Own your work from design through production deployment, operational monitoring, and business impact measurement. Accountability extends beyond the feature to CI/CD pipeline health, observability, cost efficiency, and domain-level outcomes.
Architectural Decision-Making. Make pragmatic, timely architectural choices that balance modern AI and data technologies with reliability, cost, and delivery speed. Distinguish reversible vs. irreversible decisions and move forward without waiting for consensus on the former. Document decisions in lightweight ADRs and own the outcomes.
Cross-Functional Collaboration. Partner with product, design, infrastructure, and GTM teams to translate customer and business needs into technical solutions. Operate with business awareness – understand how your systems impact revenue, customer outcomes, and strategic priorities.
דרישות:
5+ years building and shipping production-grade back end and data systems in distributed cloud environments (AWS and/or GCP).
Hands-on AI/ML integration in production workflows. You have shipped systems where AI, LLM, or agent-based components are part of the production runtime – not just prototypes or research. You can speak to the architectural tradeoffs of integrating AI into live backend systems.
Active use of AI-assisted development tooling as a workflow multiplier. You currently use AI tooling (Copilot, Cursor, or equivalent) to accelerate your engineering output and can articulate specifically how it increases your throughput. You stay current on relevant tooling without being directed to do so.
Strong back end expertise in Java (Spring Boot), Python, and/or Go. Hands-on experience with relational and non-relational databases, data modeling, and query optimization.
Demonstrated expertise in automated testing, CI/CD, and observability.
High-Velocity ownership – candidates should thrive in high-ownership, builder-first environments where shipping daily and owning outcomes are fundamental to the role.
Demonstrated ability to break work into small, incremental deliveries and maintain strong delivery flow. You have a track record of decomposing complex work into small, safely mergeable increments and shipping software solutions to solve customer pain continuously. You can describe your approach to scope deconstruction and provide concrete examples.
Preferred
Experience shipping ML-Ops powered s המשרה מיועדת לנשים ולגברים כאחד.
5+ years building and shipping production-grade back end and data systems in distributed cloud environments (AWS and/or GCP).
Hands-on AI/ML integration in production workflows. You have shipped systems where AI, LLM, or agent-based components are part of the production runtime – not just prototypes or research. You can speak to the architectural tradeoffs of integrating AI into live backend systems.
Active use of AI-assisted development tooling as a workflow multiplier. You currently use AI tooling (Copilot, Cursor, or equivalent) to accelerate your engineering output and can articulate specifically how it increases your throughput. You stay current on relevant tooling without being directed to do so.
Strong back end expertise in Java (Spring Boot), Python, and/or Go. Hands-on experience with relational and non-relational databases, data modeling, and query optimization.
Demonstrated expertise in automated testing, CI/CD, and observability.
High-Velocity ownership – candidates should thrive in high-ownership, builder-first environments where shipping daily and owning outcomes are fundamental to the role.
Demonstrated ability to break work into small, incremental deliveries and maintain strong delivery flow. You have a track record of decomposing complex work into small, safely mergeable increments and shipping software solutions to solve customer pain continuously. You can describe your approach to scope deconstruction and provide concrete examples.
Preferred
Experience shipping ML-Ops powered s המשרה מיועדת לנשים ולגברים כאחד.
