I build products people trust — and the R&D organizations that keep shipping them.
20+ years turning ambitious ideas into secure, scalable software. I own AI and ML end to end — from model development through GPU-backed, production-grade systems that respond in real time — and build lean R&D organizations that use AI to ship more, faster, for less.
How I work
I take on the roles where technology leadership is the difference between a company that scales and one that stalls — as a full-time executive, or through committed, multi-month engagements.
Head of R&D / CTO
Own technology end to end — strategy, architecture, and the organization behind it. I set direction, make the hard calls, and build a team that keeps delivering long after the first release. This is where I do my best work: in the seat, accountable for the outcome.
AI & ML, development to production
I lead AI and ML across the whole lifecycle — from model development and training through GPU orchestration, deployment, and the engineering that gets production-grade response times at scale. That includes making the models secure and robust enough to put in front of real customers.
R&D turnaround & scaling
When delivery stalls, teams burn out, or the platform can't keep up with growth, I take ownership of the problem — diagnosing the real bottleneck and rebuilding how the organization works so it ships reliably at scale. A commitment, not a drive-by review.
AI-driven engineering — more output per dollar
Bringing AI into how a team builds software is a strategic bet, and most teams get it wrong: they either ignore it or burn budget on tools that don't stick. I embed AI where it removes real toil — automating the repetitive work, keeping quality and testing rigorous — so a leaner team ships more, faster, at lower cost. Do more with less, without cutting corners.
Why work with me
Most technology problems that look technical are really about trust, clarity, and people. My job is to remove the uncertainty — so you know your product is secure, your roadmap is realistic, and your team can deliver.
I've done this across the full range: founding my own company, running R&D at a high-scale SaaS platform, directing teams inside global brands, and now leading research at the frontier of machine-learning security. That range means I can talk to your board and your engineers in the same conversation, and be understood by both.
If you're not technical, that's exactly the point. You shouldn't have to become an engineer to make confident technology decisions — that translation is what I do. And I don't do it from the outside: I take ownership, embed with the team, and stay accountable for the result.
- P.1Shipped hardware-grade software for DellLed the team that designed the software for the Dell Networking X-Series Switch — a product held to enterprise reliability standards.
- P.2Scaled a demanding SaaS platformRebuilt technology, security, and delivery at ActiveTrail — modern data infrastructure, security programs, and processes built for aggressive growth.
- P.3AI & ML from research to productionAt DeepKeep, leading model development through GPU-backed production systems built for real-time response — and recognized through publications, patents, and top-tier conference talks.
What clients say
Real words from the people I've worked with. These two are drafts — replace with the actual quotes once your contacts send them.
[Contact at Perceptiv8: 1–2 sentences on the outcome Gad delivered — e.g. what problem he solved, and the concrete result for the business.]
[Contact at Summoro: 1–2 sentences on working with Gad — what changed because he was involved, in plain business terms.]
Insights
Writing on machine-learning security, scaling engineering teams, and the technology decisions that make or break a company.
Your model works in the notebook. Now make it fast, reliable, and affordable in production.
The gap between a working model and a production system that responds in milliseconds — without a runaway GPU bill — is where most AI projects quietly stall. What it takes to close it.
How to build an engineering team that uses AI to ship more — without the runaway cost.
Bringing AI into how you build is a strategic decision, not a tooling one. Where it creates real leverage, where it quietly burns budget, and how to keep quality high while a smaller team moves faster.
Your AI feature is an attack surface. Here's what non-technical leaders need to ask.
A plain-language guide to the new risks AI introduces into a product — and the handful of questions every executive should be asking their team before they ship.
Experience
Two decades of technical leadership — from founding a company to running R&D at scale.
Lead engineers and ML researchers building technology at the intersection of machine-learning research and security. Own the strategic R&D vision and deliver next-generation ML-security products.
Built and led diverse teams of engineers and engineering managers, set long-term strategy and roadmaps, and drove cross-functional engineering efforts.
Owned infrastructure and IT for a high-scale SaaS platform. Modernized the technology stack, established security programs, and rebuilt delivery processes for scalability and fast recovery.
Ran a boutique software company. Led the team that designed the software for the Dell Networking X-Series Switch, and delivered technology migrations for enterprise clients.
Directed three development teams (10–14 people), owning systems analysis and design across all of MSN Israel's projects.
Grew from programmer to head team leader, delivering web and Windows applications, CRM, and web services.
Technical depth
For the technical reader who wants to check under the hood.
AI / ML lifecycle
- Model development & training
- ONNX · PyTorch · TensorFlow
- LLM training & serving
- Production inference & low-latency
- ML security & robustness
GPU & infrastructure
- GPU orchestration & scheduling
- GPU cluster management
- Inference optimization
- Autoscaling & cost control
- Kubernetes
Languages
- Python
- C / C++
- Java
- C#
- Rust
Platforms
- Google Cloud · Azure · AWS
- On-prem
- Kafka · Storm
- Elasticsearch · Redis
- Foundations: R&D strategy, security, scaling
Get in touch
Considering me for a leadership role, or have a serious engagement in mind? Tell me what you're building and where you're headed.
- LinkedIn/in/gadsosa →
- X@GADSOSA_HANDLE →
- LocationIsrael · Remote-friendly