You know that feeling when you get an LLM working perfectly in a playground, then realize you need to make it handle real money, real customers, and real uptime? That's where we live.
The GenAI team builds conversational experiences that feel human and decision engines that learn on their own. If moving from prototype to production with millions flowing through it gets you buzzing, keep reading.
What you'll do:
Design, build, and own end-to-end Generative-AI services from first prompt to production rollout.
Combine retrieval-augmented generation (RAG), function calling, and agents to automate underwriting, claims, and customer support flows.
Fine-tune open-source and commercial LLMs on proprietary datasets, evaluate with both offline benchmarks and online A/B tests.
Collaborate with product, data science, and design to invent new AI-first customer experiences (and kill the bad ones fast).
Build scalable APIs and micro-services (TypeScript/NestJS or Python/FastAPI) running in Kubernetes on AWS.
Instrument, monitor, and continuously improve model quality, latency, and cost you ship it, you own it
Mentor fellow engineers on GenAI best practices: prompt engineering, vector search, safety & guard-rails.
Contribute to internal tooling that automates the boring stuff (CI/CD, sandbox environments, branch deploys).
Requirements: What we're looking for:
6+ years building production-grade backend or ML systems that real users depend on.
Tangible experience with LLMs prompting, fine-tuning, or deploying them behind an API (show us what you've shipped).
Fluency in at least one backend language (TypeScript, Python) and familiarity with cloud-native infra (Docker, K8s, AWS).
Strong grounding in algorithms, data structures, and probability CS, EE, Math, or equivalent real-world chops.
Demonstrated skill in building resilient, high-traffic, "can't-go-down" services (finance, gaming, ad-tech, etc.).
Passion for agentic coding: chaining tools, orchestrating workflows, letting the machine do the grunt work.
Curiosity, pragmatism, and the ability to learn crazy-fast (zero bureaucracy, maximum building).
Ability to be in our Tel-Aviv office at least 3 days a week
Bonus points:
Hands-on with vector databases (Pinecone, Weaviate, PGVector) or semantic search systems.
Experience implementing policy, safety, or red-teaming frameworks around GenAI.
Open-source contributions or published research in NLP / generative modeling.
Prior work with voice bots, multimodal models, or real-time streaming architectures.
This position is open to all candidates.