AI, LLM and forward deployed engineers for Israeli startups.
Building an LLM feature, running models in production, or getting your AI product live at a customer's site? We find the engineers for that. Commit Offshore is the Israeli-led offshore hiring division of COMMIT. We place AI/ML, LLM, RAG, MLOps, agent and forward deployed engineers with Israeli startups, scale-ups and R&D teams, from 14 countries including Israel. Every candidate is ranked in CVCO, our database of 16,000+ profiles, screened by a senior recruiter and scored on a recorded exam. You usually see the first vetted CV within 48 hours of a confirmed brief.
AI roles we place
Here is who we place, and what each of them does for you.
| Role | What they do for you |
|---|---|
| AI/ML Engineers | Train models, test them properly and get them into production |
| LLM Engineers | Build product features on top of large language models: prompts, fine-tuning and evals |
| Forward Deployed Engineers | Work with your customers: connect your AI product to their data and systems, and get it running in production |
| AI Solution Architects | Decide how your AI product fits together, and which models and infrastructure it runs on |
| MLOps Engineers | Keep models running in production: pipelines, deployment, monitoring and the cloud bill |
| RAG Engineers | Make your AI answer from your own data: search, vector databases and grounded answers |
| AI Agent Engineers | Build agents that call tools and APIs and finish multi-step tasks on their own |
| AI Data Engineers | Build the data pipelines and datasets your AI depends on |
| AI QA / Automation Engineers | Test and evaluate AI features, and automate the rest of your testing |
Forward deployed engineers
A forward deployed engineer (FDE) is a senior engineer who works with your customers, not just behind the scenes. They sit with the customer's team, learn how the work really gets done, connect your product to the customer's data and systems, and stay until it runs in production. Then they bring what they learned back to your product team.
Palantir made the role famous. Today many AI companies that sell to enterprises hire for it, because a model that demos well still has to get through the customer's data, security review and day-to-day workflow before anyone pays for it.
When you need one
- You sell an AI product to enterprises, and every new customer needs integration work before go-live.
- Pilots stall somewhere between a great demo and production.
- Your core engineers keep getting pulled into customer calls instead of the roadmap.
- Customer feedback reaches your product team late, or not at all.
What we look for
- Engineering first. Python or TypeScript, APIs, data pipelines and the cloud your customer runs on.
- Hands-on LLM work. Prompts, tool calling, RAG and evals, with a production example they can walk you through.
- At ease with customers. They can run a workshop, scope a pilot and push back on the wrong request.
- They write things down, so the next deployment goes faster than the last.
FDE candidates go through the same process as every other role: CVCO ranking, a senior recruiter screen and a recorded exam built for the role. If your customers are in the US, UK or Canada, we can also look for FDEs based there.
What we mean by "AI-ready"
"AI-ready" gets used for almost anything, so we pinned it down. Every profile we send carries one of five labels, and you know exactly what you're getting. We only use labels 3 to 5 when the candidate has done that work in production and passed an exam built for it.
| Label | What it means | How we check it |
|---|---|---|
| 1. AI-assisted developer | Uses AI coding tools such as Copilot, Cursor or Claude Code every day, and knows when to review, test or reject what they produce. | Exam task with AI tools allowed, scored on verification and code quality. |
| 2. Senior developer, AI-enabled workflows | Everything in level 1, plus 5+ years of experience and has set up AI practices for a team: code review, tests, docs and secure-use rules. | Interview with a worked example; reference check. |
| 3. AI application engineer | Has shipped production features on LLM APIs: prompts, structured output, tool calling, and cost and latency limits. | Recorded exam extending an LLM feature; production reference. |
| 4. LLM, RAG or agent specialist | Has owned retrieval, evaluation pipelines or multi-step agents in production, and can tell you about a failure they fixed. | Role-specific exam: RAG design plus an evaluation plan; production reference. |
| 5. ML or MLOps engineer | Trains or fine-tunes and deploys models, or runs model deployment and monitoring in production. | Modelling or deployment exam; production reference. |
Want to see how a candidate really did? Ask for the exam recording and score of anyone we send. For more, see hire LLM engineers or LLM engineer vs ML engineer.
Three ways to work with us
Why Commit Offshore for AI talent
- Human + AI screening. AI matches your brief to the exact technologies the role needs. A senior recruiter then screens the shortlist, and a recorded technical exam checks the skills before you see a CV. See how we vet.
- A large pool, ready now. CVCO, our internal candidate database, holds 16,000+ candidate profiles. Only the people we shortlist for your role go on to the recruiter screen and the exam.
- Trained for AI-assisted work. Our engineers get structured training in AI-assisted development.
- Real production AI. Our engineers have shipped AI in regulated fintech (Bridgewise) and developer infrastructure (Lunar.dev).
- They stay. The engineers we place stay 4.3 years on average.
AI engineering success stories
Bridgewise
AI-powered investment intelligence.
Challenge: engineers who can ship eval-grade LLM pipelines into regulated fintech. Team: embedded ML and engineering team across two countries. Technology: LLM, machine learning, Python, cloud. Outcome: the same engineers are still scaling the platform, in a partnership that has run since 2020.
“Engineers who can ship eval-grade LLM pipelines into regulated fintech are nearly impossible to find. Commit Offshore embedded them with us, and four years later, the same people are still scaling our platform.”Elad Nachmias, CTO, BridgewiseRead the Bridgewise story →
Lunar.dev
AI agents and developer infrastructure (acquired by Boomi, 2026).
Challenge: an eval-trained AI engineer for a fixed deadline. Team: on-demand engineers added per project. Technology: AI agents, Google Cloud. Outcome: relevant candidates within days of each request, plus Google Cloud cost and setup support.
Read the Lunar.dev story →How it works
- Tell us what you need: the technology, seniority, location and timeline.
- We find the right engineers, with Human + AI screening.
- Meet your shortlist. The first vetted CV usually arrives within 48 hours, and you interview 2–3 finalists.
- Build your team. Engineers are typically onboarded within 2 weeks.
- Scale when you need to. Add or change engineers as your roadmap moves.
Questions we get asked most.
Two ways to start. Both reach Lee directly.
Book a 30-minute call to talk it through, or send us the role and get your first CV, usually within 48 hours. Either way, Lee gets back to you the same business day.