לוח משרות דרושים AI Engineer בתל אביב יפו, לחיפוש זה נמצאו 91 משרות

Requirements:
Minimum of 5+ years of professional experience in Support Enablement: Demonstrated experience in a Support Enablement, Customer Success, or Technical Support Content role, with a strong operational understanding of Customer Support workflows and knowledge base management/optimization.
Analytical Skills: Proven ability to analyze support data and provide actionable insights
AI/ML/LLM Foundation: Possesses a high-level understanding of AI tools, LLMs, machine learning, and applicative AI principles.
Technical Communication: Strong skills in translating observed model performance issues and Agentic Action requirements into clear, prioritized technical requirements for R&D teams.
Ownership: A dedicated individual who takes full responsibility for their work, driving projects to successful completion.
Data/Content Governance: Experience with data validation, content management, and implementing data governance standards, especially for knowledge bases feeding AI systems.
Advantage
Prompt Engineering: Proven expertise in systematically authoring, testing, and refining instructional inputs to drive specific model behavior.
Cloud Technologies: Experience with Cloud platforms like GCP, Kubernetes and containers
Experience with cloud cyber security technologies
Experience with Bigquery.

Requirements:
Your Experience:
Experience: 3+ years of professional software engineering experience delivering clean, maintainable code in a production environment.
Proven AI/Automation Impact: Demonstrated experience in at least one of the following areas:
Supported or led an AI transformation/adoption effort within an engineering team.
Developed and deployed a major AI-powered feature or internal productivity tool.
Built CI/CD workflows or developer tooling that utilizes autonomous AI agents.
Collaborative Mindset: Experience building tooling meant for other technical stakeholders (engineers/TPMs) and incorporating user feedback into future iterations.
Core Engineering Skills: Solid understanding of software design, API development, and data workflows.
Nice to Have:
Python Proficiency: Strong experience with Python and its associated libraries or AI frameworks (e.g., FastAPI, LangChain, or similar orchestration tools).
Security Knowledge: Familiarity with SIEM, API integrations, or general cybersecurity operations.

Requirements:
5+ years of experience in system architecture or enterprise platform engineering, with Proven experience designing and implementing technical governance frameworks in a large-scale or regulated environment.
At least 2 years working with AI/ML systems in production
Hands-on experience building LLM-powered applications or autonomous agents
Solid understanding of agentic patterns: tool use, RAG, memory models, multi-agent orchestration, HITL design
Experience with prompt engineering, evaluation frameworks, and LLM observability/monitoring
Ability to translate business requirements into scalable technical architectures - and then actually build them
Clear communicator who can work across technical and non-technical stakeholders
Experience with Claude Code / Cowork, Base44, or other Anthropic/OpenAI tooling
Nice to Have:
Background in ITSM, enterprise SaaS, or platform engineering
Experience building internal developer tools or enabling non-developer builders
Familiarity with AI governance frameworks (NIST AI RMF, ISO 42001, IMDA).

Requirements:
10+ years of hands-on experience delivering production-grade machine learning and deep learning projects at scale.
Proven ability to own the entire lifecycle of a project-taking ambiguous ideas from initial research through to successful production deployment.
Extensive experience designing, training, and fine-tuning complex models, including SLMs and LLMs, tailored to specific proprietary datasets.
Track record of shipping diverse models to production across both resource-constrained endpoints and high-throughput cloud environments.
Deep expertise in building and optimizing agentic AI systems, including RAG architectures and autonomous workflows.
Demonstrated ability to lead technical strategy, ensuring research code is scalable, reliable, and production-ready.
Advanced degree (MSc or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field.
Excellent communication skills, with the ability to articulate complex architectural decisions to both technical teams and leadership.
Preferred Qualifications
Background in the cybersecurity domain, specifically in developing AI/ML models to detect and prevent cyber attacks.
Deep cybersecurity expertise in non-AI fields, such as vulnerability research, reverse engineering, or low-level security systems development.
Experience with low-level performance engineering, including model quantization, pruning, and runtime frameworks like ONNX or TensorRT.

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:
7+ years of software engineering experience, or equivalent depth demonstrated through the systems you have built and owned.
A strong track record of building and operating production backend automation systems, engineering infrastructure, or internal platforms.
Strong Python experience and the ability to design production-grade APIs, services, and distributed workloads.
Experience building testing frameworks, automation platforms, evaluation systems, or internal developer tooling.
A solid understanding of distributed systems, APIs, microservices, and cloud-native architectures, including concurrency, reliability, and failure modes.
Hands-on experience with CI/CD pipelines and modern software delivery practices.
The ability to take ambiguous technical problems, develop pragmatic solutions, and drive them through production.
High ownership, sound engineering judgment, and the ability to work independently in a fast-moving environment.
Clear communication skills and experience collaborating across research and engineering teams.

Requirements:
Have 6+ years of backend development experience, especially with Go
Have built or worked on high-throughput, low-latency systems
Understand cloud-native environments (Kubernetes, containers, etc.)
Care about clean, maintainable, well-architected code
Love working on developer-first products and want your work to have real impact

Requirements:
3+ years of software engineering with hands-on, in-production LLM experience - you've shipped an AI-powered system that real users depend on, not just notebooks or demos.

Strong prompt and context engineering: you treat prompts as artifacts you version, test, and improve.

An eval-driven instinct: you reach for a measurement before you reach for a bigger model, and you know how to detect and reduce hallucination.

Fluency with structured outputs, function/tool calling, and multi-agent orchestration.

Solid engineering fundamentals - you build the pipeline around the model, not just call the API.

Judgment about cost, latency, and provider trade-offs at scale.

Requirements:
What we're looking for (the part that matters most)

You've built real, working software and can show it. Projects that run count for more than a CV.

Deep, hands-on control of at least one agentic coding tool: you steer it, feed it the right context, and know when to trust it and when to verify.

Solid fundamentals (memory, processes, files) and enough systems knowledge to read what the tools produce and catch what's wrong.

The discipline to verify. Here, it compiled is not the same as it's correct.

Genuine curiosity about operating systems and low-level work, and drive to learn the parts you don't know yet.

Explicitly not required

A specific degree, a set number of years, or prior Rust or systems experience in a paid job. If you taught yourself this with good tools, that's the whole point.

Requirements:
What You'll Bring

8+ years of backend engineering in high-level programming languages (e.g., Go, Python), with a proven track record of solving hard technical problems.

Hands-on experience building and shipping production LLM agent systems with frameworks like LangGraph, Strands Agents, or ADK, and with model gateways/routing layers.

A low-ego, high-impact approach: You are self-directed, driven by delivery, and willing to roll up your sleeves for the foundational work that ensures system stability.

Deep architectural pragmatism: Extensive experience with microservices, databases, and asynchronous systems, coupled with a nuanced understanding of exactly when and how to deploy them.

Forward-thinking adaptability: A continuous desire to explore new technologies, leveraging modern development tools and AI paradigms to accelerate your workflows and multiply your impact.

Bachelor of Science in Computer Science, Software Engineering, or equivalent battle-tested experience.

Requirements:
* Customer facing, account management or sales experience - with proven background in exceeding revenue targets
* Strong background in filmmaking, creative technology, content production, or media innovation
* Experience with AI creative tools and emerging production workflows.
* Excellent presentation and public speaking skills
* Comfortable presenting to executives, partners, creators, and large audiences
* Strong storytelling and visual communication abilities
* Highly organized, proactive, and able to move quickly from idea to execution

Requirements:
You may be a good fit if you have

Strong research skills in both quantitative and qualitative methods.

Strong writing skills, and experience as an academic (or industry equivalent) writer or analyst.

Interest in AI and specifically AI security.

Technical proficiency.

Background in AI/cybersecurity - a big advantage

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:
8+ years of experience in Backend Engineering or Data Engineering with a software-first mindset.
Strong proficiency in Python and SQL for data manipulation and modeling.
Experience in building high-performance services or scalable backend systems.
Deep expertise in the Modern Data Stack: specifically GCP, BigQuery, and Airflow (or similar orchestration tools).
AI/ML Infrastructure familiarity: Experience building or supporting infrastructure for LLMs, RAG applications, or managing vector databases.
Data Modeling Expert: Proven ability to design complex schemas and semantic layers that simplify data access for downstream consumers.
Architectural Ownership: You are comfortable taking a vague requirement (e.g., "map the competitive landscape") and designing the entire data lifecycle to solve it.

Requirements:
BSc in computer science, math, physics, or a related field
7+ years of experience as an AI Researcher/NLP Researcher/Applied Scientist, including experience leading or managing research teams
Proven track record of building and managing high-performing AI research / Data Science teams.
Solid grounding in core machine and deep learning concepts and techniques, data challenges (imbalance, scaling etc.), and evaluation.
Demonstrated expertise in applying LLMs - prompt engineering and prompt tuning (few-shot, chain-of-thought, tool/function calling, routing), task adaptation (instruction/SFT, PEFT/LoRA, DPO/RLHF), retrieval-augmented generation, rigorous evaluation and production deployment with appropriate safety, latency, and cost controls.
Self-learner, initiator, able to quickly learn new technologies
Experience in NLP - a significant advantage