Requirements:
Must have:
8+ years engineering experience with production systems, including time leading or tech-leading a team.
Experience building a team from the ground up - first hires, culture, hiring bar.
Shipped AI/LLM products to production - not just demos or POCs.
Strong Python and/or TypeScript.
Deep hands-on experience with LLMs, agent frameworks (LangGraph, Mastra, AWS Strands, Vercel AI SDK, or similar), prompt engineering, RAG, and model behavior in production.
Distributed systems fundamentals: workflow orchestration, fault-tolerant architectures, async patterns.
Cloud and self-hosted model deployment (Bedrock, Vertex AI, Azure OpenAI, Anthropic, Ollama).
Hands-on leadership - you write code, review PRs, set the bar. You also know when to step back.
Comfortable with ambiguity and the pace of a zero-to-one build.
Nice to have:
Background in network security, asset discovery, or traffic analysis
Familiarity with OT/ICS network protocols (Modbus, S7comm, PROFINET, DNP3)
Multi-agent architectures in production (A2A, agent swarms).
Memory libraries (Mem0, LangMem, MemGPT).
LLM evaluation frameworks (LangSmith, Bedrock Evaluations).
Vector stores (Pinecone, Weaviate, Chroma), PostgreSQL/MongoDB.
Background in cybersecurity or critical infrastructure.
IaC (CDK, Terraform).

Requirements:
Senior engineering depth: You have 6+ years of experience building and operating production software, with strong backend and distributed-systems fundamentals and experience designing APIs and services.
Shipped agents, not demos: You have taken an LLM agent system with tool use, multi-turn interaction, and planning to real users, and you can talk concretely about how it failed and what you did about it.
Architectural judgment: Informed opinions on single-agent vs. multi-agent design, orchestration patterns, and the current framework and SDK landscape, with the pragmatism to pick the boring option when boring wins.
Eval discipline: You have built or owned evaluation for an LLM system, including golden datasets, LLM-as-judge with calibration, and regression gates in CI, and you can quote the metrics you moved.
Tool design instincts: You know when a deterministic tool beats a model call, how to design tool contracts an LLM will not misuse, and how to keep cost and latency under control.
Distributed systems fluency: Streaming, stateful services, and the operational instincts to keep long-running agent sessions alive in production.
Ownership in ambiguity: You can lead an area as a hands-on IC in an early-stage environment with little existing structure. Security domain experience such as SIEM platforms, SOC workflows, detection engineering, or security query languages, and experience with modern agent SDKs and protocols such as MCP, are strong advantages.

Requirements:
Genuine full-stack experience - Hands-on, professional experience across both front-end and back-end development. Hands-on experience with React on the front end (our standard stack), and equal comfort on either side of the stack.
7+ years of professional software engineering experience, spanning both front-end and back-end work
Proven experience owning real-time, mission-critical systems where low latency, correctness, and high availability are non-negotiable
BSc in Computer Science or equivalent hands-on experience
A builders mindset, with a track record of taking complex products from idea to production and scaling them
Comfortable working across the stack - from user-facing interfaces and APIs to data pipelines and infrastructure
A modern engineering mindset, with strong instincts for using automation and AI tools to increase impact and velocity
A high bar for quality and a willingness to challenge yourself and the team
Clear communication skills and the ability to collaborate effectively in small, senior teams.

Requirements:
Requirements
8+ years of engineering experience, with at least 2 years focused on LLM-based systems, agents, or applied ML in production.
Agentic Systems Expertise: Hands-on experience building multi-agent architectures, tool-calling workflows, and orchestration frameworks (e.g. LangGraph, CrewAI, ADK, or custom).
Prompt Engineering & Evals: You treat prompts as code - versioned, tested, and measured. You know how to systematically debug and improve LLM behavior.
AI-Native Development: You actively use agentic coding tools (Claude Code, Cursor, etc.) to accelerate your own workflow.

Requirements:
Your Impact
Design and implement automated evaluation loops, static analysis, and rigorous quality gates to ensure the ADLC process doesn't just write code, but consistently produces great, production-ready code.
Help the team tackle complex, hard problems to elevate our autonomous development product from "good" to "excellent".
Lead complex initiatives in Context Engineering and Prompt Engineering.
Manage and orchestrate the complex ecosystem of autonomous agents utilized for internal development.
Serve as a leading individual in a very strong team professionally and personally - Were looking for someone who not only delivers his own work but improves that of those around them.
Find space for growth to push the entire team or group forward - New projects, changing processes or improving existing tools.
View prompt engineering as a core engineering discipline-where rewriting agent behavior is a versioned, reviewed, and tested code change.
Act with a debugging temperament; conduct deep-dive analyses of raw agent transcripts to diagnose non-deterministic failures and ascertain root causes instead of merely working around them.
Your Experience
At least 8+ years of experience in software development, architecture, or owning operational systems in production.
Computer Science B.Sc. or equivalent education or equivalent military experience required.
A product builder's mindset: you can extract requirements, talk to stakeholders, and tell the difference between what's important and what's noise.
Experience in building production grade agents. Deep understanding of the agent loop, its states and transitions. You know how to build it correctly, not just use it.
Positive can-do mindset, able to work independently and within a team.
Hands-on experience with LLM APIs, including a practical, highly-skeptical understanding of token costs, caching, context windows, and model failure points.
You know how to build the right context for a task, including memory systems, session storage, and vector databases.
You understand where LLMs fail and how to design around those failure points.
You've used traces or observability tooling to diagnose and improve agent behavior.
A systems-level background that touches reliability, observability, or platform engineering, with a strong preference for writing narrow, deterministic code over building hypothetical abstractions.
Experience in the cybersecurity space - an advantage.

Requirements:
You've shipped agentic systems to production - real orchestration, tool use, structured outputs, and the failure modes that only appear at scale. Not "I've called an LLM API."
You've built the evaluation discipline, not just consumed it: trajectory tests, golden datasets, regression gates, offline replay. "It seems better" is not a metric, and you have opinions about what is.
Deep backend and distributed-systems engineering. Strong Python, and comfort with workflow orchestration (Temporal or equivalent), streaming, and cloud-native infrastructure. Agent platforms are systems problems wearing an AI hat.
Fluency across the modern agent stack - LangChain/LangGraph-style frameworks, multi-provider routing, structured output contracts, prompt and context engineering - with the judgment to know which parts are load-bearing and which are fashion.
Security literacy. Enough to reason about whether an agent's security output can be trusted, and to argue with researchers on the merits. You don't need to be a vulnerability researcher.
Influence without authority. You'll change how three teams work with no one reporting to you. Show us where you've done that.
Advantage: experience with AI/LLM security - red-teaming agents, prompt injection, or agentic attack patterns.
Advantage: background in cybersecurity, detection engineering, or WAF/mitigation systems.
Advantage: you've driven AI adoption across a whole company, not only an engineering org.

Requirements:
You may be a good fit if you:

6+ years of software engineering experience, with a track record of shipping complex systems to production

Strong understanding of LLMs and transformer architecture, and how model behavior shapes what agents can do

Able to mine and analyze data to build agents that learn and improve continually

Hands-on experience building agentic systems: tool calling, planning, multi-step or long-running task execution

Experience with agentic frameworks (e.g. LangChain, DeepAgents) and tracing tools (e.g. LangSmith)

Strong grasp of context engineering and tool interfaces for frontier LLMs

Comfortable defining the metrics and evaluations that prove a system works, and iterating on them

Strong product judgment; you turn vague needs into reliable systems and ship without waiting for perfect specs

Thrive in a small, fast-moving team and take ownership end to end

Requirements:
10+ years of software engineering experience with a strong backend background.
3+ years as a Tech Lead or technical leader in fast-paced environments.
Strong AI engineering skills, including context engineering, agent optimization, troubleshooting, and systematic evaluation of AI system performance.
Excellent system design and distributed systems experience.
Strong understanding of scalable cloud architectures (AWS or equivalent).
Experience building production-grade backend services and microservices.
Excellent communication and stakeholder management skills.
Passion for solving complex technical problems.
Curiosity and excitement about AI and rapidly evolving technologies.
Ability to lead technical direction while remaining hands-on.

Requirements:
Agentic Expertise: Deep experience with LLMs and the modern agentic stack (LangGraph, AutoGPT patterns, tool-calling, and orchestration). You understand how to guide an LLM through complex, multi-step tasks.
The "Full-Stack" DS Mindset: You are a coder first. You are comfortable digging into a large codebase, understanding backend services, and writing production-grade code. You don't wait for someone else to "fix the API."
Product-Driven Research: You are obsessed with impact. You choose the right tool for the job-whether its a simple heuristic or a complex fine-tuned model-based on what provides the most value to the user.
Data & System Fluency: Strong experience with Python and SQL. You understand how to interface with Postgres and ClickHouse to build the data-rich contexts our agents require.
Engineering Rigor: You care about version control, testing, and CI/CD. You treat your prompts and model configurations with the same engineering discipline as code.
The company Mindset: You take ownership, act with accountability, collaborate openly, and focus on delivering meaningful impact. You thrive in fast-moving environments, embrace ambiguity, and enjoy solving hard problems together.
Education: Bachelors or Masters degree in CS, Math, Statistics, or equivalent practical experience in a high-growth AI environment.
Communication: Full professional fluency in both Hebrew and English.

Requirements:
7+ years of software development experience, with 2+ years leading and mentoring engineers.
Strong expertise in backend development (Go, Python, Node.js, or similar) and familiarity with modern frontend technologies (React, Vue, TypeScript, etc.).
Solid understanding of developer experience - how to build frameworks and tooling that enable other teams to move faster.
Experience working with or integrating AI systems, LLMs, or agent-based architectures.
Excellent collaboration skills and a startup mindset - hands-on, pragmatic, and impact-oriented.
A product-oriented mindset and the ability to work in a fast-paced, team-driven environment.
Advantages:
Experience with agent orchestration, context management, or retrieval-augmented architectures.
Experience in cybersecurity.
Hands-on expertise in Go development.
Excellent leadership, communication, and problem-solving skills.
Ability to thrive in fast moving environments with high ownership and autonomy.