דרושים » מוצר » Director of AI Enablement

This role sits at the intersection of customers, Engineering, Data Science, GIS, Sales, Marketing, and Customer Success. You will translate validated market needs into scalable products, clear technical requirements, and prioritized roadmaps.

The ideal candidate combines strong product strategy, technical fluency, customer discovery, and hands-on product ownership, with experience building B2B technology products in data-rich or infrastructure environments.



Key Responsibilities


Product Strategy & Roadmap

Own and evolve the product roadmap in line with company and revenue goals.
Shape the positioning and evolution of Recover, EarthWorks, EO Discover, and future SaaS, DaaS, API, and AI offerings.
Prioritize scalable, recurring product opportunities over one-off customer requests.
Build business cases and define product KPIs to support investment and prioritization decisions.


Customer & Market Discovery

Conduct structured Voice of Customer research with utilities, engineering firms, agencies, and partners.
Translate customer workflows into clear problem statements, personas, and Jobs-to-be-Done.
Validate concepts through prototypes, MVPs, pilots, and market feedback.
Assess market size, adoption potential, and willingness to pay.


Product Ownership & Delivery

Translate strategy into epics, features, user stories, and acceptance criteria.
Own and prioritize the backlog based on customer value, strategic fit, dependencies, and effort.
Partner closely with Engineering, Data Science, UX, and GIS through planning, refinement, development, and release.
Coordinate UAT, define release criteria, and ensure clear traceability from customer problem to measured outcome.


Technical Product Leadership

Work across SaaS, cloud platforms, APIs, integrations, AI/ML, geospatial analytics, remote sensing, and SAR.
Translate technical capabilities into customer-facing products and workflows.
Understand architecture, data flows, integrations, and technical trade-offs well enough to work effectively with R&D.
Challenge technology-led development that lacks clear customer or market value.


Go-to-Market & Product Performance

Partner with Marketing, Sales, and Customer Success on ICPs, packaging, pricing, positioning, launches, pilots, and POCs.
Support strategic customer and partner engagements.
Track product success through adoption, retention, revenue, customer value, quality, and time-to-value.
Use customer feedback, product usage, and competitive insights to drive continuous improvement.


Product Operations

Improve intake, prioritization, release-management, and product governance processes.
Build practical templates and dashboards for roadmaps, business cases, launches, and product performance.
Strengthen coordination across Product, Engineering, Operations, Sales, Marketing, and Customer Success.

we are looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments.
This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value.
The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface. It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current. You will make pragmatic build-versus-buy decisions and choose the simplest approach-search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation-that meets each use case.
Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve.

We are looking for a Product Owner to join us and help shape our companys tactical product ecosystem. In this role, you will translate capabilities into features and detailed PRDs, including acceptance criteria, edge cases, and the level of definition engineering teams need to execute efficiently. You will work from early-stage product direction- including customer conversations, CTO concepts, operator feedback, and emerging ideas-and operate across the product ecosystem, acting as the connective tissue between products rather than owning a single product. A key focus of this role is driving communication solutions across the ecosystem, ensuring products connect, interoperate, and reliably exchanging tactical data in the field.
RESPONSIBILITIES
Translate capabilities into features and PRDs with acceptance criteria, edge scenarios, and clear definition
Drive the implementation of communication solutions across the ecosystem, from capability definition through integration with engineering
Research feasibility and tradeoffs in response to customer and business requests, and come back with a clear position
Spend real time in the field with operators to understand how the product is actually used
Participate in customer-facing technical discussions and demos as the product counterpart
Ensure cross-product coherence across the ecosystem
Partner with System Engineering, UX, QA, and Operations to move capabilities from concept to deployed.

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.