דרושים » הנדסה » מומחה /ית AI ואוטומציה לעולמות הבנייה והתכנון

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.

As an AI Engineer, you will help build scalable deep learning systems for Physical AI and humanoid robotics.
You will contribute across the full AI lifecycle, from simulator integration and large-scale data pipelines to model training, evaluation, deployment, and optimization on real robotic platforms.
In this role, you will work with large-scale datasets and compute infrastructure to train state-of-the-art models that enable robots to perceive, reason, and act in the physical world.
You will help build robust, production-grade systems that bridge simulation and reality, accelerate model iteration, and bring cutting-edge AI capabilities onto real humanoid robots.

we are looking for a Senior AI Engineer.
About the Role
In this role, you will:
Build Modular AI Capabilities: Design and implement self-contained "AI Skills" that connect to the CYE Platforms core data layers.
Ship End-to-End Autonomous Agents: Build agents that don't just "think," but act. You will deploy agents that handle the heavy lifting of the security lifecycle.
Bridge the Gap to Non-Technical Users: Create the abstractions that allow our customer success teams and client stakeholders to trigger or chain these AI workflows.
Own the AI Infrastructure & Evaluation: Maintain and contribute to the environment where these agents are born, ensuring they are grounded in truth.

We're looking for an AI Workflow Engineer to join our System Architecture group. This is a high-impact role where you'll design, build, and continuously improve AI-powered workflows, agents, and tools that help our architects do their best work - from system design and modeling through documentation, technical decision-making, and review.
Working from within the architecture team, you'll turn the group's real, day-to-day pain points into practical AI-assisted solutions. Your primary focus is system architecture within the software development lifecycle (SDLC), and because architecture touches everything, your work will naturally extend into adjacent stages wherever AI can add value.
Key Responsibilities:
Design and build AI-powered workflows, agents, and internal tools that support system architecture work - design, modeling, documentation, architecture decisions, and reviews.
Develop and iterate on agents within our internal framework, using agentic AI techniques to accelerate and augment architecture and engineering tasks.
Continuously improve existing workflows and tools as team needs evolve and AI capabilities advance.
Partner closely with the System Architecture team to understand real pain points and turn them into practical, AI-assisted solutions.
Measure and demonstrate the impact of the workflows you build - for example on design-cycle time, review turnaround, and the quality and completeness of architecture documentation and decision records.
Build proof-of-concept projects that show the practical value of AI in real architecture and engineering workflows.
Develop best practices, prompt engineering guidelines, and reusable patterns for AI-assisted architecture and development.
Stay current with the rapidly evolving AI tooling landscape and advise on strategic adoption.

As a Senior AI Engineer in the MSC IL AI Innovation team, you will lead the design, development, and productization of advanced AI solutions for our Specialized Clouds, with a strong focus on sovereign-first, secure, and responsible AI systems.

You will operate as a technical leader, owning end‑to‑end AI architecture and delivery, influencing platform direction, and mentoring engineers while working closely with architects, product managers, security, and operations teams. This role goes beyond implementation; you will shape how AI is built, governed, and scaled across sovereign and regulated cloud environments.


Responsibilities

Design and build AI agents that plan, use tools/APIs, manage state/memory, and reliably complete multi-step workflows.

Own AI features from design through production, including deployment, monitoring, and live‑site reliability, with an eval-first development lifecycle: define success criteria, build evaluation datasets and automated harnesses, and run human-in-the-loop reviews where needed.

Develop and maintain prompt, retrieval, and memory strategies (system prompts, few-shot examples, tool schemas, retrieval context) with proper versioning and evaluation coverage.

Debug AI behavior using prompt analysis, data inspection, and model/tool-call traces, and translate failure patterns into targeted improvements.

Establish and track AI quality metrics (e.g., accuracy, groundedness, relevance, hallucination rate) and integrate them into CI/CD release gates.

Optimize runtime performance and economics (token usage, inference cost, latency, caching, model selection/routing, batching) and implement monitoring and continuous improvement loops (online signals, drift detection, structured user feedback).

Partner with product, design, and domain stakeholders to define use cases, acceptance criteria, and rollout plans for AI features.

Live site responsibility.

In this role, you will be a key contributor to the design and implementation of AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.

You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of NPU embedded solutions, actively promoting AI capabilities to the customers.

What will you do:

Own and design key components of the AI Graph Compiler software stack for NPU-based systems.

Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.

Collaborate on HW-SW co-design, influencing NPU architecture.

Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.

Mentor junior engineers and contribute to technical best practices.