לוח משרות דרושים הייטק AI משרה מלאה, לחיפוש זה נמצאו 251 משרות

We're looking for a Senior Project Manager who combines strong technical depth with exceptional leadership and execution skills.

As a key member of the LevelUP organization, you will:

Lead strategic AI transformation programs across engineering organizations.

Drive adoption of AI-powered tools, workflows, and best practices.

Partner with senior technology leaders, architects, engineering teams, and product organizations.

Manage complex, cross-functional initiatives involving multiple global teams across Israel and the United States.

Define success metrics, track business impact, and communicate progress to executive leadership.

Navigate ambiguity, remove blockers, and ensure successful delivery in a fast-paced environment.

Were seeking a passionate Senior AI Engineer to join our AI team. With a wide array of responsibilities and corresponding impact, youll help spearhead our new product offering and the world-class AI conversation product impacting millions of patients.

Responsibilities
Design, develop, and drive design processes and products that incorporate Generative AI (LLMs), models and knowledge Retrieval in order to help patients across the US.
Design and define data pipelines and evaluation pipelines.
Build applicative AI in production on a large scale.
Build solutions for different mediums, including voice products with minimal latency and high accuracy.
Provide a scientific evaluation of the quality of our models.
Continuously improve our existing NLP capabilities to help patients across the US answer any question, schedule appointments with their doctor, refill their prescriptions, and much more.

We're looking for a hands-on Senior AI Engineer to provide technical leadership within our Generative AI team.
This role is designed for engineers who enjoy combining deep hands-on development with technical leadership. You'll drive technical decisions, shape solution architecture, mentor engineers, and help define engineering best practices while remaining actively involved in designing and building production-grade AI solutions.
Working closely with customers and multidisciplinary teams, you'll help transform complex business challenges into scalable AI systems that deliver measurable business value.
What You'll Do:
Lead the technical design and delivery of end-to-end GenAI solutions, from architecture through production deployment.
Design, build and maintain production-grade AI applications and backend services using Python.
Develop LLM-powered solutions, including retrieval-based architectures, AI agents and orchestration workflows.
Drive architecture decisions for prompt engineering, retrieval pipelines and model evaluation.
Translate business requirements into scalable AI architectures and technical solutions.
Provide technical leadership through design reviews, code reviews and engineering mentorship.
Contribute to engineering standards, best practices and technical excellence across projects.
Evaluate emerging AI models, frameworks and technologies, incorporating them into production solutions where appropriate.

We are seeking an experienced Generative AI Engineer to join our AI squad. This is a unique opportunity to wear multiple hats - serving as both a developer of cutting-edge GenAI solutions and an advisory expert helping organizations transform their AI capabilities. You'll build end-to-end GenAI projects from conception to production while staying at the forefront of this rapidly evolving field.
Key Responsibilities:
GenAI Development & Implementation:
End-to-End Development: Build GenAI solutions from POC through production deployment, handling all backend development responsibilities
Client Engagement: Participate in technical discussions with clients, gather requirements, and help translate business visions into feasible technical solutions through presentations and consultations
Backend Development: Design and implement production-grade microservices architectures for GenAI applications using Python
Cloud Implementation: Deploy and manage GenAI solutions across GCP, Azure, and AWS platforms, leveraging cloud-native AI services
Cross-functional Collaboration: Work closely with project managers, full-stack developers, and Power Automate teams to deliver complete solutions
System Evaluation: Assess and optimize production-grade GenAI systems for performance, scalability, and reliability
Continuous Learning & Innovation:
Technology Scouting: Continuously explore and evaluate new GenAI models, frameworks, and techniques as they emerge
Best Practices Development: Establish and refine methodologies for GenAI solution development and deployment

we are looking for a Data AI Engineer.
Join our global Data team, which partners closely with Product, Engineering, and Business stakeholders to power data-driven decision making and AI-driven products across . The team operates in a fast-paced, high-growth environment, building AI solutions, data foundations, and analytical frameworks that directly influence product strategy and customer outcomes.
What youll do
Design and build LLM-powered systems that directly improve product capabilities and customer experience - enabling Product, Engineering, and Business teams to make faster, smarter, data-driven decisions at scale
Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance
Continuously monitor, refine, and evolve AI models and solutions - iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities and shifting business needs.
Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed and engineering velocity across the team
Analyze large-scale datasets to surface strategic insights, define and track critical KPIs, and translate analytical findings into actionable product and business recommendations.

We are looking for a talented and self-motivated Annotation Specialist to join our Annotation team and work closely with our Algorithms department.

This is a unique opportunity to make a real impact in a meaningful, safety-critical industry.

Responsibilities:
Be responsible for the ground truth annotation and tagging and channel accurate visual data to the deep learning pipeline.
Work with various imagery sensors such as RGB, Thermal and visual geographical data.
Work closely with internal teams to understand their requirements for annotated data.
Suggest methods for improving work efficiency.

we are looking for a Senior AI Researcher.
This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.
The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.
Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.

We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.
You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.
What Youll Do:
Build, train, and ship detection models end-to-end, from raw data to production
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities
Own evaluation: design benchmarks, build labeled datasets, and define production standards
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability
Iterate fast, with a tight feedback loop between model performance and product outcomes

The mission:

Take this from concept to a category-defining business. Zero to outcomes. You will:

Live in the market until you understand it better than anyone in our company.

Define the strategy, the wedge, the offering, the pricing, and the brand.

Build the early product and the early GTM in parallel.

Land the first paying customers yourself.

Build the team you need to scale once we have signal.

Think of it as founding a startup with us as your first investor, your distribution advantage, and your operational backbone, rather than as a corporate initiative.

What you'll own end to end:

Market. Talk to 50+ AI Implementers in your first 90 days. Map the segments (technical builders vs business strategists, project-based vs retainer, generalist vs vertical specialists). Map the communities, the influencers. Decide which segment we go after first and why.

Strategy. Build the business case, the product thesis and the GTM foundations. What's the offering? What's the wedge? What's the pricing model? What does the unit economics look like at scale? What are the four to five things we have to be opinionated about, and what are we deliberately not doing?

GTM. Design the acquisition motion from scratch. Community, content, partnerships, paid, sales-led, product-led, some combination. Test cheap, kill what doesn't work, double down on what does. Treat GTM as foundational, not as something we figure out after product is built.

Product. Partner with engineering and design to ship the first version. You won't build it alone, but you'll set the bar for what it is, what it isn't, and what good looks like. Customers. Land the first 10 paying customers personally. Get on the calls. Read every email. Watch every demo. Be the most informed person on what's working and what isn't.

We're looking for a AI Backend Engineer with a data science foundation to join our AI team and build production systems that integrate Generative AI capabilities. You'll design and implement end-to-end solutions APIs, databases, orchestration, and cloud infrastructure for clients across a wide range of sectors.
This role is ideal for someone who thinks in systems, enjoys working with GenAI technologies, and wants to grow into designing complex architectures. You'll work alongside senior engineers on real delivery projects from day one.
Key Responsibilities:
Design and build backend systems APIs, databases, authentication, and integrations
Implement multi-agent architectures and orchestration workflows combining LLMs with backend logic
Deploy and maintain systems on GCP, Azure, or AWS
Optimize AI system performance, cost, and reliability
Collaborate with client teams, DevOps, and stakeholders on project delivery
Contribute to technical discussions and solution design.

At our company, we build AI-powered vision systems that enhance safety and decision-making for some of the worlds largest vessels.
Our platform processes live video streams from multiple onboard cameras to provide real-time situational awareness, detecting and tracking marine objects, even in low visibility and highly congested environments. These systems directly support navigational decisions and help prevent collisions, reduce human error, and improve operational efficiency.
Our systems are already deployed across thousands of vessels and have processed hundreds of millions of nautical miles of real-world data, operating in unpredictable and safety-critical conditions.
This role sits at the intersection of AI and high-performance systems engineering, focused on solving real-world problems under strict constraints. You will work on systems where performance and reliability are critical and where improvements have a direct, measurable impact on real-world safety.
This is a senior, systems-focused role with end-to-end ownership over performance and reliability of production computer vision pipelines. You will define optimization strategies, identify bottlenecks across the system, and drive improvements under real-world constraints.
What youll do
Build and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server)
Take models from research and turn them into production-ready, reliable components deployed on vessels
Profile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessing
Identify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordination
Make and justify tradeoffs between latency, accuracy, stability, and resource utilization
Design and implement robust data and inference pipelines (video -> model -> actionable output for crew)
Develop benchmarking and evaluation workflows to measure performance end-to-end and support release gating
Build and improve observability tools, including logging, monitoring, and debugging workflows for production systems
Define and maintain clear interfaces between research code and production systems
Work closely with research and backend teams to integrate new models into production systems
Continuously improve system efficiency and reliability under hardware and runtime constraints.

This is a role for someone who wants to build the org of the future, where agents and people work side by side, and is ready to own the program that gets a company there! Youre taking over the leadership of our AI Transformation: the internal AI work across every function. Youll own the program end-to-end.

What Youll Do?
Run the three pillars (Adoption, Infrastructure, Builds) and the foundation that ties them together. Adjust as we learn, and make it real on the ground.
Lead the change, not just the tooling - the hard part isnt building the agent, its getting people to change how they worked for years. Youll drive that change, build trust with VPs, and push where it matters.
Manage and grow the team - both direct reports and dotted line champions. Youll grow the team from there as the program scales.
Run the prioritization machine - choose whats the right effort, from vertical low hanging fruit and all the way to horizontal initiatives.
Make agents as employees real - every agent has a manager, a cost, a performance review, and a forward plan. Managers are measured and rewarded on how their agents perform.
Measure and report impact - hours saved, adoption, leverage and metrics you define and instrument. Report up. Adjust the program based on what the numbers say.
Stay hands-on - this isnt a role for someone who only writes plans. You build agents and workflows yourself when it makes sense, sit in proposal reviews with the PM, weigh in on architecture with the Engineer, and ship things when needed.

Location: Tel Aviv. Hybrid.

We're building the financial infrastructure that powers global innovation. With our cutting-edge suite of Embedded payments, cards, and lending solutions, we enable millions of businesses and consumers to transact seamlessly and securely. With 900+ employees worldwide and an R&D center of over 160 employees in Jerusalem - were reshaping how financial technology is developed and delivered..
The Role:
As AI capabilities accelerate across the bank, we need an engineer to design and enforce safe AI usage-protecting customer data, preserving model integrity, and meeting our regulatory obligations. You'll be the architect of guardrails, tooling, and policies that make AI both secure and useful for product and internal teams. This isn't about slowing things down; it's about building the trust layer that lets innovation move fast without breaking things.
Who You Are:
You're a security engineer who's excited about the AI wave-someone who sees LLMs and Agentic AI as fascinating puzzles to secure, not just threats to mitigate. You've spent 5+ years in Security Engineering, AppSec, or Cloud Security, and at least 1-2 of those years have been spent getting your hands dirty with LLMs, AI Agents and MCPs. You understand how agentic frameworks (LangGraph, CrewAI, AutoGen, and similar) orchestrate multi-step tool use-and where trust boundaries break down. You've assessed or secured AI-powered coding agents (Claude Code, GitHub Copilot, Cursor) and understand the unique risks of AI with filesystem, terminal, and API access in Developer environments. You're equally comfortable dissecting a prompt injection attack as you are writing a Terraform module or shipping a Python library. You know your way around AWS and/or Azure, modern app stacks ( Python /TypeScript, REST/gRPC, containers/Kubernetes), and can translate security requirements into Developer -friendly tooling-not just PDF policies that gather dust. You communicate clearly in English and Hebrew, thrive in regulated environments, and understand that security in financial services means mapping controls to frameworks like FFIEC, SOC 2, and PCI DSS-and actually having the evidence to prove it.
What Youll Actually Be Doing:
* Design enterprise AI guardrails across Azure and AWS (e.g., Azure AI Studio/Azure OpenAI, Amazon Bedrock/SageMaker): content filtering, PII redaction, prompt/response validation, and policy enforcement services.
* Assess and define secure usage patterns and data governance controls for agentic frameworks and coding agents: permission scoping, tool-call authorization, leastprivileged retrieval, session isolation, and MCP server governance.
* Threat model AI systems (apps, agents, MCPs, RAG, fine-tuning pipelines) using frameworks like STRIDE and the OWASP Top 10 for LLM Apps; define misuse scenarios (prompt injection/context poisoning/jailbreaks/ data exfiltration) and build mitigations.
* Build monitoring and telemetry: privacy-preserving prompt/response logging, sensitive- data detection, safety/eval dashboards, drift/abuse signals, and incident hooks into our SIEM.
* Integrate AI security into the SDLC: reusable libraries, pre-commit checks, CI/CD gates, policy-as-code, and secure-by-default reference architectures for product teams.
* Evaluate thirdparty AI vendors and internal apps: security reviews, data residency and retention requirements, SSO/SCIM integrations, DPA/TPRM inputs, and continuous control testing.
* Partner across Security, data, Privacy, and Engineering to map AI controls to FFIEC, SOC 2, and PCI DSS; document control evidence for audits.
* Lead/participate in AI redteaming: automated jailbreak/promptinjection tests, safety benchmarks, purpleteam exercises, and response playbooks for AI incidents.
* Enable the org with concise guidelines, examples, and training on safe AI development and usage.
 
Why Youll Love Working Here:
* Flexible hybrid.

The AI Forward Deployed Engineer (FDE) is responsible for driving the successful adoption of AI solutions across our Groups business units.

The role focuses on translating business needs into practical AI solutions and ensuring they are effectively embedded into real workflows, delivering measurable business impact.

FDEs work closely with business teams to identify high‑value opportunities, deploy and build customized AI solutions, and continuously improve outcomes. Success is measured by adoption, usage, tech quality and business value.


Key Responsibilities
Partner with business units and tech teams to identify and prioritize AI use cases.
Own AI initiatives end‑to‑end: from problem definition to deployment and impact measurement.
Deploy and adapt AI solutions to fit real operational workflows.
Drive user adoption through training, iteration, and feedback.
Support continuous improvement and scaling of successful solutions.

Join us to build cutting-edge systems, collaborate with world-class engineers, and shape how autonomous software is built in production.
We are looking for an AI Engineer to join the hunt.
Responsibilities:
Take part in the design and development of AI-driven features for our Dev-Native Observability Platform.
Collaborate with cross-functional teams to integrate AI solutions into existing systems.
Contribute to the product roadmap by identifying AI opportunities and providing insights as a potential end user
Stay updated with the latest AI trends and technologies to ensure our platform remains cutting-edge.