לוח משרות דרושים הייטק AI  בירושלים, לחיפוש זה נמצאו 21 משרות

we are looking for an experienced AI Solutions Architect to join our team and support the design and implementation of AI solutions across organizational systems. The role focuses on designing AI-based solutions to improve existing business processes, supporting development teams in implementing AI solutions, and defining scalable, secure, and compliant AI architectures. The position combines AI architecture, Cloud technologies, technical consulting, and cross-functional collaboration. This is not a pure hands-on development role; rather, it requires strong technical leadership, architectural thinking, and the ability to guide development teams throughout the implementation lifecycle.
Key Responsibilities
* Design and architect AI-based solutions for organizational and enterprise-wide use.
* Support development teams in planning and implementing AI solutions.
* Provide technical guidance and participate in POCs, including involvement at the code level when required.
* Define best practices and guidelines for the effective use of AI models.
* Develop technical procedures and guidelines for development teams while ensuring compliance with regulatory and information security requirements
* Design cross-organizational AI solutions, including solutions for making organizational data and information more accessible.
* Work with external vendors and partners on AI-based projects, including technical guidance, coordination, and quality control.
* Troubleshoot complex technical challenges and provide architectural solutions.
* Lead and coordinate technology initiatives and work closely with multidisciplinary teams.
* Support teams in adopting AI technologies and integrating them into existing systems and processes.

We are looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables our algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow us to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.
As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.
What will your job look like?
Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for our developers.
Expand the capabilities of existing platforms by embedding smart, context-aware LLM features.
Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them.
Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG).
Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves.

We are hiring someone to lead product level and internal team AI end-to-end: from protocol and architecture decisions to server-side implementation to the AI logic that decides when and how our tools are used, to internal processes that improve R&D and product development efficiency. This is a hands-on position with managerial aspects, not an advisory role. You will be building hands on in parallel to building the process and managing/guiding the team.

What You Will be Doing
Own the strategy and roadmap for C2As AI strategy within R&D and AI agents specifically,
Lead the AI adoption effort technically within the product and across the organization as the company learns how to become more efficient with AI usage for R&D.
Design and build MCP servers that expose our APIs as well-described, reliably usable tools for AI clients.
Build the AI decision layer - the prompting, tool schemas, and agentic logic that let a model select the right API, pass the right parameters, and recover gracefully from errors.
Implement complete, production-grade features end to end: backend services, data models, APIs, and the AI integration on top, not prototypes.
Define how AI-accessible APIs are scoped, authenticated, and permissioned, so an agent can only ever do what it is authorized to do. Security is the product, this matters.
Build evaluation and observability for the AI layer: measure tool-selection accuracy, catch regressions, and trace agent behavior in production.
Set engineering standards and mentor others as the effort grows into a team.
Implement new features required by our cybersecurity product.

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.