דרושים » תוכנה » AI Engineer

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer to join our founding team. The focus of this role is to design, develop, and deploy autonomous AI agents that can automate and optimize complex enterprise workflows. You will work on building intelligent systems capable of decision-making, data extraction, document processing, and other tasks typically requiring human intervention. This is an opportunity to influence the architecture and strategy of AI-driven automation solutions for large-scale enterprise environments.

Your Impact
AI Agent Development

Design and develop autonomous AI agents using best of breed large language models to automate tasks such as document processing, data extraction, and workflow management.

Implement reinforcement learning techniques to enhance decision-making capabilities of AI agents.

AI Integration

Build and integrate APIs that connect AI agents with external systems and enterprise software (ERP, CRM).

Use frameworks like TensorFlow, PyTorch, or Hugging Face to deploy and optimize AI models for real-time processing.

Data Processing and Pipelines

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data efficiently.

Optimize data handling to improve speed and accuracy of AI agents.

Security and Compliance

Implement authentication and authorization mechanisms to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g., GDPR, HIPAA) and adopt best practices for secure data handling.

Cloud Infrastructure and Scalability

Deploy AI agents on cloud platforms (AWS, GCP, or Azure) ensuring scalability and reliability.

Leverage containerization (Docker, Kubernetes) for efficient deployment and management.

Testing and Optimization

Develop and execute unit, integration, and performance tests for AI-driven systems.

Continuously monitor and optimize system performance for speed, accuracy, and cost-efficiency.

Collaboration

Work closely with AI researchers, front end engineers, and product teams to align AI agent capabilities with business requirements.

Participate in code reviews, design discussions, and architecture planning to drive innovation.

We are a well-funded, early-stage startup looking for a Frontend / Fullstack Engineer to join our founding team. In this role, you will help build cutting-edge web applications and platforms that bring autonomous AI agents to life, automating complex enterprise workflows. You will play a critical role in shaping intuitive, responsive UIs and robust backend APIs that connect AI-driven agents to real-world enterprise systems (ERP, CRM, and more).

This is an opportunity to influence the architecture, design, and user experience of next-generation AI-powered automation solutions for large-scale enterprise environments.

Your Impact
Product & Frontend Development

Design and develop modern, intuitive, and responsive frontend interfaces that allow users to interact seamlessly with AI agents and automated workflows.

Create user dashboards, workflow builders, and visualization tools to monitor AI-driven automation in real time.

Collaborate with design and product teams to bring complex AI capabilities into simple and usable UI experiences.

API and Fullstack Engineering

Develop robust backend APIs that connect AI agents to enterprise data sources and external systems (ERP, CRM, document management systems).

Implement REST and GraphQL APIs to expose AI agent functionalities to the frontend.

Handle real-time communication, event streams, and notifications to keep users informed of workflow progress.

AI Integration & Collaboration

Collaborate closely with AI/ML engineers to integrate AI agent outputs into the product, including document processing, data extraction, and decision-making workflows.

Manage and present AI agent-generated data in user-friendly formats and visualizations.

Team Collaboration & Culture

Work closely with AI engineers, product designers, and enterprise customers to align AI capabilities with real business needs.

Participate in code reviews, architecture discussions, and feature planning.

Help shape the companys engineering culture and best practices as part of the founding team.

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer specializing in infrastructure to join our founding team. The focus of this role is to build and scale the infrastructure that powers autonomous AI agents automating complex enterprise workflows. You will own the systems, pipelines, and platforms that let our AI agents run reliably, securely, and at scale in production.

Your Impact
Infrastructure & Platform

Design, build, and own the core infrastructure powering our AI agent platform, from data pipelines to production deployment systems.

Build and scale the backend systems that support high-throughput document processing and data extraction workloads.

Cloud Infrastructure and Scalability

Architect and deploy infrastructure on cloud platforms (AWS, GCP, or Azure) with a focus on scalability, reliability, and cost efficiency.

Own containerization and orchestration (Docker, Kubernetes) for all production workloads.

Build and maintain CI/CD pipelines and DevOps practices that let the team ship fast without breaking things.

Data Infrastructure

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data at scale.

Build the infrastructure layer connecting AI agents to databases, vector stores, and enterprise systems (ERP, CRM).

API & Systems Integration

Build and maintain robust, well-documented APIs connecting AI agents with external systems and enterprise software.

Design for reliability: retries, observability, and graceful degradation across distributed systems.

Security and Compliance

Implement authentication and authorization mechanisms (OAuth2, JWT) to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g. GDPR, HIPAA) and drive best practices for secure data handling across the infrastructure.

Monitoring and Optimization

Build observability and monitoring systems to track infrastructure health, performance, and cost.

Continuously optimize system performance for speed, reliability, and cost-efficiency at scale.

Collaboration

Work closely with AI/ML engineers, product, and the founding team to make sure infrastructure decisions support fast iteration and production-grade reliability.

Participate in code reviews, design discussions, and architecture planning to drive infrastructure strategy.

we are looking for a AI Engineer - Algorithm Team.
As an AI Engineer, you will join a fast-moving, highly technical team that works at the intersection of computer vision, deep learning, and modern AI. You will be the driving force behind our LLM and agentic AI efforts - exploring, evaluating, and deploying the latest research and tooling in this rapidly evolving space, and shaping how we apply it across the organization to turn cutting-edge ideas into production-ready tools and agents.
You will design and build LLM-powered applications, agentic workflows, and internal tools that empower our algorithms, mapping, and engineering teams to move faster and operate at greater scale. Collaboration is central to the role, you will work closely with researchers, engineers, and domain experts to identify high-impact opportunities, prototype quickly, and integrate reliable AI systems into real-world products.
Key Responsibilities:
Design, build, and iterate on LLM-powered applications, including retrieval-augmented generation (RAG) systems, agents, and fine-tuned models tailored to unique data and workflows.
Develop agentic workflows that automate complex, multi-step tasks across research, data analysis, and engineering pipelines.
Build internal tools and assistants that empower algorithm researchers, mapping experts, and engineers to work faster and more effectively.
Evaluate and integrate the latest foundation models, frameworks, and techniques (e.g., prompt engineering, fine-tuning, tool use, multi-agent orchestration), keeping pace with rapid advances in the field.
Design robust evaluation methodologies to measure quality, reliability, and safety of LLM-based systems.
Collaborate closely with the Algorithms, Mapping, and Software teams to identify high-impact opportunities and transition prototypes into reliable, production-grade systems.

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

We're backed by tier-1 global VCs, led by second-time founders, and already deployed with organizations operating at serious scale. AI is not a feature here - it's the system.
We're hiring a Senior AI Engineer to design, fine-tune, and operate AI agents and large-scale models in production. This role exists because off-the-shelf models aren't enough for the problems we're solving.
If you enjoy pushing models until they break - and then fixing them - keep reading.
What you'll do:
Design and operate AI agents that reason, act, and collaborate with humans
Fine-tune and adapt large language models for:
Behavior analysis
Reasoning over long, messy timelines
High-precision enterprise workflows
Build agent orchestration systems (tool use, memory, planning, feedback loops)
Run large-scale inference and training pipelines in production
Work on model evaluation, drift detection, and continuous improvement
Optimize for latency, cost, and reliability at real enterprise scale
Partner closely with DevOps, security, and backend engineers - no research silos
Ship models that are auditable, explainable, and safe in sensitive environments

We're looking for an AI Engineer to join our team. As an AI Engineer , youll be a key member of our founding team, designing, building, and deploying cutting-edge AI systems that leverage large language models (LLMs), unstructured data, and advanced ML techniques. Youll be instrumental in bringing AI from research to real-world production, delivering high-impact features that drive our security platform.

In this role, you'll have a unique opportunity to solve complex problems, work with large scale data and be part of the core team that shapes the future of AI in the company.


WHAT YOU WILL DO

Design and develop end-to-end AI solutions, from data ingestion and modeling to deployment and observability.
Build and fine-tune LLM-based applications to tackle complex cybersecurity challenges.
Own the full ML lifecycle - from ideation and experimentation to production readiness.
Collaborate with product, engineering, and security teams to turn AI research into user-facing features.
Work with large-scale unstructured data (e.g., logs, threat intel, alerts) to extract meaningful insights.
Evaluate and optimize AI system performance, scalability, and reliability.
Contribute to core architectural decisions related to AI and ML infrastructure.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

Required AI Engineer & Architect
The Role:
As AI Architect in the ACoE, you are the technical backbone of our internal agentic transformation. You will design and build the reference architectures, standards, and shared infrastructure that enable hundreds of AI agents to run reliably, safely, and at scale - across R&D, Sales, Customer Revenue, HR, Finance, and Marketing.
This is a hands-on, highly visible role. You will split your time between deep technical work (designing agent orchestration patterns, building cross-company automations, evaluating platforms) and enabling others (code reviews, technical mentorship of citizen developers, setting standards). You will report directly to the ACoE Lead and work closely with the CTO, BI, and Platform teams.
Key Responsibilities
Architecture & Standards:
Define and maintain our reference architectures for agent orchestration, tool permissions, memory models, inter-agent communication, and data boundaries
Establish technical standards for agentic development - prompt engineering patterns, evaluation harnesses, testing frameworks, and shared agent templates
Drive tooling standardization across in partnership with the CTO and CFO, and lead the annual vendor/platform review
Build & Enable:
Develop and maintain selected cross-company agentic solutions and automation workflows
Build and maintain the shared infrastructure: monitoring integrations, KPI dashboards, the agent registry, and the ACoE knowledge base
Conduct technical reviews and code reviews for agents before production deployment
Serve as technical SME for citizen developers across all business units - unblocking, guiding, and reviewing their work
Governance Support:
Define and implement the pre-deployment evaluation harness and model card standards
Support the AI Governance Officer (initially the ACoE Lead) with technical input on risk classification, incident response, and rollback planning
Contribute to quarterly ethics audits for high-risk agents
Innovation:
Track the rapidly evolving agentic AI ecosystem and bring relevant insights and tools back
Co-author external technical content (whitepapers, conference presentations) to establish our technical thought leadership.