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Requirements:
1-2 years' hands-on experience in a technical/customer support role (support teams, NOC, or similar) - a support background, not a general business-operations or analyst one.

Real familiarity with case queues, SLAs, and escalations gained from doing the work.

An analytical mindset and comfort with Salesforce; exposure to Tableau/PowerPoint/Confluence is a plus.

A process mindset - you think how do we stop this from happening again, not just how do we close this case.

Good written and verbal English; Hebrew is an advantage.

Requirements:
5+ years of finance experience, including solid hands‑on FP&A work.
2+ years of finance team management, preferably leading an FP&A team.
Strong hands-on FP&A skills, including budgeting, forecasting, and financial analysis.
Business partnering capabilities with the ability to influence decision-making.
Strong analytical and communication skills, with the ability to simplify complexity.
AI-mindset - Proactively looks to move processes to AI‑driven solutions. Curious, hands‑on, and comfortable experimenting with new tools while maintaining strong judgment and data integrity.
Advanced Excel skills; experience with BI or financial systems is an advantage.
Fluent English (written and spoken).

Requirements:
3-5+ years of experience in Enterprise IT Support, Field Engineering, or End-User Computing environments.
Strong experience supporting Microsoft 365 environments.
Hands-on experience with Active Directory and Microsoft Entra ID.
Experience supporting Windows and macOS devices.
Experience managing IT inventory, assets, and hardware lifecycle processes.
Experience working with procurement teams, vendors, and service providers.
Fluent English communication skills, both written and verbal.

Requirements:
Core Responsibilities
Design and implement end-to-end data digestion pipelines for complex unstructured and semi-structured inputs.
Integrate classical algorithms with LLM-centric workflows to produce high-quality contextual inputs for reasoning tasks.
Build systems for segmentation, structure extraction, semantic decomposition, embedding-based representations, and graph construction.
Develop agentic workflows that combine tools, retrieval, and reasoning for interactive customer experiences.
Design evaluation suites, datasets, metrics, and regression tests to measure quality, robustness, and performance over time.
Continuously refine workflows based on customer usage, failure analysis, and measurable improvements.
What We're Looking For
Required Qualifications
MSc (or equivalent experience) in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field.
5+ years of experience in data science / algorithm development / applied research engineering roles.
Strong experience building complex algorithmic workflows beyond model calls - including multi-stage pipelines, feature extraction, structure inference, and optimization.
Deep understanding of LLM digestion concepts: RAG, context engineering, chunking strategies, retrieval and ranking, tool usage patterns, and reliability techniques.
Experience designing and maintaining evaluation frameworks for both classical algorithms and LLM workflows (offline + online, regression, benchmarking).
Strong programming skills in Python; familiarity with TypeScript is a plus.
Ability to thrive in a high-ownership startup environment: ambiguity, speed, responsibility, and continuous learning.
Nice To Have
Familiarity with vector databases and scalable retrieval architectures.
Familiarity with agent orchestration frameworks (e.g., LangChain) and tool-driven reasoning systems.
Experience deploying AI workflows into production with monitoring, guardrails, and iteration loops.