דרושים » הנדסה » AI Engineering Team Manager

We are looking for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. This is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship.
You will work from our Kfar Saba site alongside RAN, PHY, and software teams, building production-grade AI solutions for real cellular networks.
What you'll do:
Define the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.
Drive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches.
Prototype and implement critical components while setting engineering and code-quality standards.
Lead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback.
Establish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.
Mentor engineers through architecture discussions, design reviews, and code reviews.
Collaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions.
Contribute to technical roadmap discussions and customer-facing architecture discussions.

The Director of AI Enablement is both a builder and a transformation leader, someone who can identify opportunities, design and implement AI solutions, and drive adoption across the organization to create measurable business value.

Reporting directly to the CEO, this is a highly visible, hands-on leadership role at the intersection of business transformation, technology, and organizational change.

The successful candidate will lead AI roadmap, build practical AI solutions, drive adoption across functions, and help transform how work is performed throughout the organization.

What Will You Do?

Define and execute AI roadmap.

Identify, prioritize, and implement high-impact AI opportunities across the business.

Partner with business, operational, engineering, and corporate functions to understand workflows, pain -points and improvements opportunities.

Translate business challenges into practical AI use cases and measurable outcomes.

Design, build, pilot, and scale AI-powered solutions, including AI agents, copilots, automations, knowledge assistants, and business applications.

Partner with IT, Engineering, Infrastructure, and Security teams to ensure scalable, secure, and sustainable solutions.

Lead the transformation, not just the technology; drive AI adoption across departments and help leaders redesign processes and ways of working to maximize AI value.

Work hand-in-hand with HR and business leaders to lead the organizational transformation required to successfully adopt AI, build internal capabilities, and embed new ways of working across the company.

Establish and lead the core pillars of the AI Enablement program: Adoption, Infrastructure, and Solution Development.

Build and lead a community of AI Builders and Leaders across the organization.

Establish AI governance, Responsible AI practices, and security standards.

Measure and communicate business impact through adoption, productivity gains, efficiency improvements, time savings, and ROI.

Success is measured not only by building AI solutions, but by driving meaningful improvements in productivity, efficiency, quality, decision-making, and business performance across the organization.

We are looking for a AI-Driven 4G PHY SW Tech Lead to work on the architecture, implementation, and real-time optimization of the LTE Layer 1 (PHY) software stack. You will be a technical authority for the domain, working across MAC, System, Integration, QA, and Product Management to deliver carrier-grade Open RAN solutions. A core part of the role is advancing how the team applies AI and agentic software development to a large, high-performance codebase, accelerating design, debugging, and delivery.
What you will do:
Lead the design, development, and evolution of the LTE Layer 1 (PHY) software stack.
Drive PHY features end to end: architecture, implementation, optimization, testing, and production support.
Drive performance optimization, debugging, and root cause analysis for carrier-grade real-time reliability.
Shape the software architecture and technical roadmap of our Open RAN platform.
Mentor engineers, set the bar for engineering practice, and partner with MAC, System Engineering, Integration, QA, and Product Management.
Apply agentic software development and LLM-based workflows, and evaluate new tools as they emerge.

We are looking for a hands-on wireless systems engineer to lead the development of a multi-RAT digital twin for our Open RAN solution. The digital twin will execute production RAN software-beginning with scheduler and MAC behavior-in a closed loop with PHY, channel, UE, traffic, and network models. It will allow engineering teams to design, evaluate, and compare features for LTE, 5G NR, and 2G without requiring a dedicated physical radio setup for every development cycle.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
What you will do:
Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin
covering LTE, 5G NR.
Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations
through stable and maintainable interfaces.
Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.
Extend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and
2G stacks without duplicating the platform.
Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.
Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.
Build representative datasets and experiment pipelines; establish conventional algorithmic baselines;

We are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.
What you will do:
Define high-value RAN ticket-intelligence use cases, acceptance criteria, evaluation metrics, and quality guardrails.
Build and maintain representative, versioned evaluation datasets using resolved tickets, Root-Cause Analysis, logs, test evidence, code changes, reviews, reassignment history, and outcomes.
Establish current-tool and non-AI baselines before evaluating new LLM, RAG, search, or agent-based approaches.
Evaluate approved internal, commercial, local, and open-source solutions using secure and reproducible data-handling processes.
Measure retrieval quality, groundedness, diagnosis accuracy, citation support, routing recommendations, calibration, abstention, latency, cost, and human effort.
Design held-out, time-based, edge, and adversarial test cases while preventing data leakage and future-outcome contamination.
Analyze failures and turn incorrect conclusions, misrouting, unsupported claims, and missed evidence into prioritized improvements.
Prototype improvements in search, metadata, context construction, prompting, reranking, classification, agent workflows, and model selection.
Develop reusable evaluation pipelines, tools, services, APIs, dashboards, or documented workflows.
Work closely with AI, RAN, QA, System Integration, Release, Field, data, and engineering teams to review results and support evidence-based decisions.

We are looking for a hands-on Computer Vision & AI Algorithm Engineer to develop advanced imaging algorithms for a cutting-edge medical device. The role spans the full spectrum of algorithm development - from creating new solutions to improving, maintaining, validating, and integrating them into the product. The ideal candidate is curious, pragmatic, and comfortable moving between deep learning, classical computer vision, data analysis, debugging, validation, and product integration as needed. They measure success by seeing algorithms work reliably in a real-world product and enjoy collaborating across disciplines to solve the challenges that move the product forward.

Responsibilities may include the following and other duties may be assigned:
Design, develop, maintain, and continuously improve AI, computer vision and image processing algorithms for a medical imaging product.
Own the full lifecycle of algorithm development, from concept and data definition through implementation, validation, product integration, and long-term maintenance.
Use both classical computer vision methods and machine/deep learning methods to come up with solutions for various problems in the domains of image classification, segmentation and 3D modeling.
Collaborate with SW developers, biomedical engineers, product specialists and other algorithms developers to refine and enhance existing applications.
Maintain and improve algorithm robustness, performance and reliability in production environments.
Identify opportunities for innovation and design novel algorithms to address complex technical challenges.
Stay at the forefront of relevant academic research by reviewing the latest papers and evaluating their potential impact on the companys algorithmic stack.