Sr. AI Backend & Data Engineer
Build and ship AI-native backend and data systems in Go/Python/Java that power real-time GTM decisions, integrating ML/LLMs as runtime dependencies and shipping daily.
- Compensation
- Not disclosed
- Location
- Herzliya, Tel Aviv, Israel
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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 spea
- 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 specifica
- 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
Nice to have
- Experience shipping ML-Ops powered systems in production (model serving, monitoring, retraining pipelines).
- Experience with distributed data technologies (e.g., Parquet, Athena, or similar query engines).
- Experience making and documenting architectural decisions autonomously (ADRs or equivalent lightweight decision records).
Full description
About the Team
This team builds an AI-native predictive analytics platform that embeds ML and AI-driven insights directly into production Go-To-Market workflows — powering real-time decisions at scale. The team owns the full stack from distributed data pipelines and backend services to ML and AI-powered capabilities that ship directly to customers.
We are building toward a model where AI components are first-class runtime dependencies, not bolt-on features. Agentic AI development is a core part of how we increase engineering velocity and deliver customer value. This is a team that ships daily, iterates constantly, and treats speed as a capability to be deliberately improved.
The Role
This is a high-ownership, builder-first Sr. Software Engineer role. You will design, build, and ship AI-integrated data systems from concept through production — owning outcomes end-to-end, including deployment, monitoring, cost, and business impact.
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.