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Required AI Full-Stack Engineer
Our Continuous Security Validation enables companies to challenge, assess and optimize their cyber-security posture against the evolving cyberthreat landscape, simply and continuously.
With world-class clients we strive to bring the highest level of service to everything we do. Our team is made up of the very best people for the job and as we grow, were always on the lookout for people with the skills, experience, and personality that will let us both shine. With high scores on Gartner, G2 and Glassdoor, our clients and employees have let us know what they love about us.
What will you do:
Design, build, and maintain production-grade AI-powered applications and services.
Develop end-to-end full-stack solutions, including backend services, APIs, and modern React-based frontends.
Build scalable, secure, and highly available distributed systems.
Develop AI agents, copilots, autonomous workflows, and intelligent automation.
Build and deploy RAG (Retrieval-Augmented Generation) solutions and integrate LLMs into production.
Design AI orchestration workflows using frameworks such as LangChain, LangGraph, or similar.
Implement AI evaluation, tracing, and observability pipelines using Langfuse or equivalent tools.
Develop MCP (Model Context Protocol) integrations and AI tooling ecosystems.
Build and maintain cloud infrastructure, CI/CD pipelines, and Infrastructure-as-Code.
Drive engineering productivity through AI-assisted development and automation.
Collaborate closely with Product, Design, and Engineering teams to deliver customer value.
Research and evaluate emerging AI technologies and introduce innovative solutions.
Own features end-to-end-from architecture and development to deployment, monitoring, and continuous improvement.

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 seeking a highly skilled and motivated AI Developer to join our growing R&D team. In this role, you'll design, develop, and scale our next-generation backend platform, AI-driven workflows, and GenAI applications. You'll work with modern cloud technologies and LLMs to build resilient, high-performance systems while contributing to architecture, technical leadership, and mentoring.
This role is ideal for an experienced backend engineer who enjoys solving complex technical challenges, building scalable systems, and working with cutting-edge AI technologies.
Key Responsibilities
Design and develop scalable backend services using Python.
Build resilient distributed systems, microservices, and AI-driven workflows.
Integrate Large Language Models (LLMs) using AWS Bedrock and other AWS services.
Design and implement event-driven architectures and modern APIs.
Optimize databases, search capabilities, and caching strategies to support high-performance applications.
Contribute to cloud infrastructure, CI/CD pipelines, and observability practices.
Collaborate with Product, Data Science, and Engineering teams to deliver scalable solutions.
Provide technical leadership through architecture discussions, mentoring, and hands-on development.

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.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.
Responsibilities:
AI Agent Design & Integration:
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development:
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure:
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement:
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.

We are looking for a talented and motivated Software Engineer with hands-on experience building and operating multi-agent AI systems in production to join our Automation Platform (DAP) team.
The team develops automation tools, orchestration capabilities, and intelligent platforms that simplify the deployment, management, troubleshooting, and optimization of large-scale network and AI infrastructure environments.
You will work on the design and development of AI-powered systems that bridge networking, automation, observability, and distributed infrastructure - running on Kubernetes at scale. Our stack includes LangGraph, Langfuse, RAG pipelines, MCP, and agent-to-agent (A2A) communication patterns.
This role combines strong software engineering with practical AI application development, with a sharp focus on production hardening, tracing, evaluation, and safety of agentic systems - not model training or research prototypes.

We are looking for one architect to own both layers. Your foundation is deep platform and DevOps architecture: reliability, scalability, security, and cost efficiency of the cloud platform every R&D team builds on. Your growth edge is the AI layer: designing how agents are orchestrated, how they access tools and data safely, how we evaluate whether they work, and how we govern their cost and behavior in production.

We know agentic AI is a young discipline. We are not looking for a decade of AI experience that does not exist. We are looking for a proven platform architect who has spent the last one to two years genuinely building LLM-based and agentic systems in production, and who wants to own where this field goes inside a company that takes it seriously.

What Youll Do:
Platform & DevOps
Cloud Architecture: Own the architecture of our cloud platform, including GKE fleet design, VPC networking, IAM/Workload Identity, and multi-environment strategies across GCP (and AWS where relevant).
IaC & GitOps: Lead Infrastructure-as-Code (Pulumi, Terraform) standards, ArgoCD deployment patterns, and secure CI/CD paved paths (GitHub Actions, GitLab CI) adopted by all R&D teams.
Reliability & Observability: Own reliability architecture, including Disaster Recovery (DR) strategy and drills, P1 incident reduction, MTTR improvement, and observability platform architecture (Datadog, Prometheus, Grafana, OpenTelemetry) including usage and cost optimization.
FinOps: Drive FinOps as an architectural discipline-rightsizing, idle-resource elimination, unallocated-spend attribution, and cost-aware design reviews using tools like Kubecost or GCP Cost Management.
Agentic AI

Agent Architecture: Design our agentic AI ecosystem, focusing on agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen), tool/MCP (Model Context Protocol) interfaces, vector databases/memory strategies (e.g., Pinecone, Qdrant, pgvector), and production AI deployment patterns.
Evaluation & Guardrails: Build the evaluation and guardrail layer for AI in production using frameworks like Ragas, TruLens, or LangSmith. Define permission models, human-in-the-loop checkpoints, audit logging, and policy-as-code for AI usage.
AI FinOps & Monitoring: Own AI cost observability, token spend monitoring, model routing strategies (optimizing for cost/quality/latency trade-offs), and real-time alerting on runaway model usage.
Reference Architectures & Standards: Define reference architectures for teams building AI features, RAG patterns, prompt versioning/management, and model API standards (OpenAI API, Anthropic, open-source models via vLLM/Ollama), driving high-leverage AI automation across R&D.

abra’s Data & AI Division is looking for a AI Consultant to join our team! We are looking for a AI Consultant to design and implement end-to-end AI solutions tailored to our customers' business needs. In this role, you will work directly with clients to identify AI opportunities, build intelligent solutions, and drive AI adoption across organizations. Full-time | Central Israel Key Responsibilities
* Understand customer business processes and identify opportunities to create value with AI.
* Design and implement end-to-end AI solutions.
* Develop AI Agents, Skills, Plugins, and MCP integrations.
* Build automations and integrations between AI platforms and enterprise systems.
* Guide customers through AI implementation and adoption.
* Stay up to date with the latest AI technologies and best practices.

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.