We are seeking an experienced and visionary Software Architect with proven, hands-on experience in Generative AI (GenAI) to join our team.
In this key role, you will be responsible for designing high-level software structures, integrating AI-driven capabilities, making critical technical decisions, and ensuring the architectural integrity and innovation of our software solutions.
What you will actually do:
Own the technical arc of an engagement
Run discovery with business and technical stakeholders and convert vague ambition ("we want AI in our support flow") into a scoped, measurable, buildable system - or into a defensible recommendation not to build it.
Say no to use cases that AI will not serve well. Deciding what not to build is a first-class deliverable here, and you will occasionally have to say it to the person signing the contract.
Partner with sales on technical pre-sales: solution shaping, effort and cost estimation, architecture defense in front of a customer's CTO or security board.
Design AI systems that survive contact with production
Architect agentic systems end to end: task decomposition, tool and function calling, orchestration and hand-off patterns, state and memory, human-in-the-loop checkpoints, failure containment and recovery.
Engineer context, not just prompts - retrieval strategy, chunking and indexing decisions, hybrid and graph retrieval, re-ranking, caching, context window budgeting, and the data pipelines that keep all of it fresh.
Make and defend the model-portfolio call: frontier vs. small vs. open-weight, hosted vs. self-hosted, when fine-tuning or distillation actually beats better retrieval, and how to stay swappable as the frontier moves every quarter.
Design for interoperability using emerging agent standards (MCP, A2A and successors) rather than locking a customer into one vendor's orchestration layer.
דרישות:
Required - you have personally done these, not managed someone who did:
Shipped GenAI to production and lived with it. At least one non-trivial LLM-based system that reached real users and that you supported afterward. You can describe what broke, how you found out, and what you changed. Demos, POCs, and hackathon projects do not count.
Built or owned an evaluation harness. You can explain how you knew your system was getting better, and what you did when a model provider's silent update regressed it.
Deep software architecture fundamentals. Distributed systems, API and integration design, data modeling, security, and operational thinking on at least one major stack (.NET, Java, Python, or Node.js) and at least one major cloud (Azure, AWS, or GCP). GenAI without this foundation produces expensive prototypes.
Strong Python. It is the working language of this field, regardless of what the surrounding system is written in.
Agentic system design experience. Multi-step, tool-using systems - and a clear-eyed view of when a deterministic workflow beats an agent.
Cost and latency engineering. You have made a system meaningfully cheaper or faster without wrecking quality, and you can show the numbers.
Consulting-grade communication. You can hold a room of skeptical senior stakeholders, disagree with a customer's architect without losing the account, write a document a CFO and a staff engineer both understand, and work across an unfamiliar business domain within days.
Strong advantages:
Prior consulting, professional services, or solution architecture experience in a multi-customer environment.
Regulated-domain delivery - finance, healthcare, defense, public sector.
Open-source contributions, published technical writing, or conference talks in the AI space.
Data engineering or platform depth (streaming, lakehouse, vector and hybrid search infrastructure).
Classical ML background - you know when a gradient-boosted tree is the right answer and an LLM is not.
Team or practice leadership.
B.Sc. in Computer Science, Engineering, Mathematics, or equivalent המשרה מיועדת לנשים ולגברים כאחד.