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our company builds the production-grade AI backbone for top-tier enterprises. We don't just wrap LLMs. Our platform autonomously handles mission-critical workflows, replacing operational friction with seamless, predictive experiences. We are a lean, engineering-driven company working directly with the biggest players in the market.
An FDE at our company is a customer-embedded engineer who owns the technical success of an AI deployment end to end. You don't advise and hand implementation to someone else. You are the implementation owner. You take an ambiguous business problem, break it into solvable technical components, build the solution on our platform, and stay with it until it works reliably in production.
What You'll Do
Own deployments end-to-end. From signed deal to production go-live and stabilization. No hand-offs.
Build production AI agents. Design and ship agent flows on our platform: prompt engineering, orchestration, edge cases, and the testing discipline that makes them production-grade.
Integrate into brownfield reality. Legacy systems, APIs, CRMs, telephony and customer infrastructure, while navigating security, compliance and incomplete requirements.
Debug in environments you don't control. When production misbehaves inside the customer's stack, you find it and fix it.
Push back with judgment. Challenge poor requirements, find creative alternatives, and align technical and business stakeholders behind them.
Be embedded with customers. On-site several days a week during active deployments, often as the most senior technical person in the room. On-site days carry dedicated compensation on top of your salary, and every production go-live carries a milestone bonus.

We are looking for a Principal Engineer who lives at the frontier of AI,
someone who can envision and build autonomous agents that reason about complex infrastructure, make intelligent decisions, and execute large-scale migrations with minimal human intervention. This is a rare opportunity to define the architecture of an AI-native platform from the ground up.

Key job responsibilities

Define and architect the technical vision for an Agentic AI migration platform, where autonomous agents discover, plan, and execute full data center exits.

Design and build multi-agent systems that leverage foundation models, chain-of-thought reasoning, and tool-use patterns to solve complex migration challenges.

Architect AI-powered network transformation capabilities, using generative models to analyze, replicate, and optimize enterprise network topologies.

Provide technical leadership across 4 scrum teams, instilling an AI-first engineering culture and ensuring architectural coherence across agent frameworks.

Drive innovation in agentic orchestration to continuously improve autonomy and accuracy for migration of servers, storage, networks, and applications.

Mentor senior engineers on AI-native development practices and cultivate a culture of rapid experimentation, spec-driven engineering, and high velocity releases on Brownfield projects.

We are looking for a Product-Driven Senior Full Stack Engineer who wants to do more than build features from a backlog. In this role, you will own entire product surfaces end-to-end - from concept to production - shipping AI-powered features that users actually touch.
Youll work directly with LLMs, APIs, and modern full-stack technologies to build experiences that feel polished, fast, and intelligent.
If you care about how something works as much as that it works, and you want to shape products that matter, this role is for you.
What Youll Do
Own features end-to-end: Design, build, and ship complete product experiences - frontend, backend, and AI integration. No hand-offs.
Build with AI: Integrate LLMs and AI APIs into real product workflows, including prompt engineering, API orchestration, and intelligent UX patterns.
Ship with taste: Make decisions about UX, polish, and user experience. Attention to detail matters.
Move fast: Work in short cycles, ship to production, learn from real users, and iterate.
Make calls: Operate with autonomy and make decisions even when information is incomplete.

Key job responsibilities
* Daily involvement in all aspects of physical design chip development (RTL2GDS), including floorplanning, synthesis, clock tree synthesis, place and route, static timing analysis, power and noise analysis, physical verification testing, and equivalence checks.
* Being actively engaged in design-backend convergence aspects and defining timing constraints.
* Taking full end-to-end responsibility for the physical design of macros and clusters level, according to specifications, under challenging constraints, with focus on optimizing power, area, and performance.
* Participation in the development of design flows, using a variety of EDA tools and vendors such as Synopsis and Cadence.
* Engaged in defining implementation and signoff methodologies.

we are looking for a Senior Full Stack Engineer.
What you'll do:
Own features end to end: design, build, ship, and run them in production.
Work closely with product on what to build, with a say in scope and priorities.
Own the operational side: DevOps, automation, and tooling sit with the engineers who build the product, not a separate team.
Build AI capabilities into the product and own how they behave in production.
Orchestrate coding agents across the SDLC and in daily work, and raise the velocity and quality of what the team ships.
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we are looking for a hands-on Tech Lead to join our R&D. Reporting directly to the Chief Architect, you will step into a rare opportunity with a fast-growing company to shape the technical future of our platform. You will serve as a technical anchor for the organization, driving the architectural evolution of our high-scale, cloud-native infrastructure built on AWS and Kubernetes, while pioneering the integration of AI across our product and engineering workflows.
Responsibilities:
Drive the technical roadmap for Firefly's platform, working closely with the Chief Architect: architecting new systems and features and continuously improving existing ones.
Lead architectural decisions across the platform's core surfaces: services, data pipelines, workflow orchestration, and multi-tenant infrastructure.
Stay hands-on in the code: build critical components, prototype new capabilities, and lead by example inside the development teams.
Establish the platform foundations: shared services, libraries, and standards used across teams.
Architect AI-powered capabilities into Firefly's product and drive AI-assisted development workflows that amplify engineering productivity across the org.
Drive end-to-end technical solutions, from API design and service boundaries to data modeling and deployment.
Build proofs-of-concept for critical paths and high-risk decisions, and act as the technical anchor for projects from design through delivery.
Write design documents, RFCs, and architectural decision records that drive cross-team alignment and capture the reasoning behind technical decisions.
Mentor engineers across teams through design and code reviews, and act as a focal point for technical questions across R&D.

Were looking for a Data & Analytics Team Lead to manage and grow our data team and own the data layer behind our AI. Youll lead a team of data analysts responsible for neural-network model analysis, model performance tracking, customer-facing and internal BI. Youll act as both the people manager and the technical lead for the team, setting technical direction while staying hands-on.
Youll thrive in this role if youre passionate about turning data into better AI products, enjoy solving complex analytical problems, and like working at the intersection of Data, Analytics, and Artificial Intelligence.
Responsibilities:
Lead the data strategy supporting AI model development, evaluation, and continuous improvement
Define KPIs and monitoring frameworks for AI model performance.
Analyze NN model outputs, build and maintain model-tracking and performance-evaluation workflows
Drive close calibration with the Algo, Product, and Data Engineering teams to align data definitions, pipelines, and metrics
Stay hands-on with SQL, Python, BI tooling, and the modern data stack
Build and own agentic data workflows - designing the context, tooling, and data access that let AI agents reliably query, analyze, and act on company data

We are looking for a skilled and motivated Field Engineer to join our technical team. In this role, you will be responsible for the end-to-end installation and configuration of our systems at customer sites worldwide. This position is ideal for a hands-on professional who enjoys working with both complex software environments and physical infrastructure. The role includes significant on-site work and frequent travel to support clients in Israel and abroad.
Responsib\ilities:
On-site Installation: Lead physical deployments, including racking, cabling, and connecting power and networking infrastructure at client sites
System Configuration: Install and configure Ubuntu/Linux-based environments and ensure seamless integration with client networks
Tier 2 Support & Maintenance: Provide technical support to international customers during installation and as part of ongoing post-installation support
Networking: Troubleshoot and optimize network connectivity (TCP/IP, DNS, firewalls) to ensure system stability
Documentation: Maintain technical deployment guides and site visit reports

A full-time Software Engineer role where you will take ownership of core features across the stack - mainly from backend to frontend - and contribute to the architecture, scalability, and performance of our company's AI-driven healthcare platform.
Key Responsibilities
Own core features end-to-end, from backend to frontend
Contribute to architecture, scalability, and performance of our AI platform
Work closely with R&D, Product, Clinical, and Data teams
Design and implement intelligent, production-grade systems leveraging LLMs, serverless infrastructure, and real-time data.

A full-time Applied AI Engineer role focused on building and optimizing our LLM-powered clinical compliance engine. You will design, implement, and refine AI systems that analyze clinical documentation, detect compliance issues, and generate actionable insights for healthcare providers.
Key Responsibilities
Design and implement LLM-based rule engines for clinical compliance detection
Build RAG systems and semantic search pipelines for clinical documentation
Optimize prompts, fine-tune models, and evaluate AI system performance
Collaborate with clinical experts to translate regulatory requirements into AI workflows
Monitor and improve AI accuracy, latency, and cost efficiency in production
Develop evaluation frameworks and testing strategies for AI outputs.

We're looking for a Senior Software Engineer to own real parts of it.
What you'll actually do?
Own entire pieces of the product- from "here's a vague problem" to "it's running for enterprise customers".
Design systems that survive real scale: multi-tenant, high-volume, real-time, always-on.
Move fast with AI agents, and stay completely responsible for what lands in production.
Make the team better: your tooling, your workflows, whatever you figured out last weekend.

As Operations Associate you will be responsible for the correct onboarding, monitoring and day-to-day operation of a subset of our partners. They are essential to our profitability.

You will partner with the CSM, Product and Data teams to ensure nothing falls between the cracks.

The main tasks expected are:
Timely onboard any new customer to the external providers you are responsible of.
Monitor performance and raise issues to the product team for those to be fixed.
Identify improvement paths to the exchange of new data and propose solutions.
Automate as much of the work as possible so you can focus on higher value added tasks.

What you'll do:
Track, measure, automate and debug revenue generation processes around dispute representment and technical related integrations.
Oversee our partnerships with revenue generation partner companies.
Track margins and collections for all customers and work with the CS team to enforce needed actions.
Partner with Product to automate repetitive CS tasks and build quicker processes to ensure smooth collaboration between us and our customers, as well as internally.

-6+ years of experience in machine learning engineering or applied AI, with a preference for an MSc or PhD in a relevant field.
Hands-on experience building and deploying large-scale production systems with machine learning components.
Practical experience with modern AI architectures, including LLMs, RAG pipelines, vector search, and prompt orchestration.
Strong backend engineering experience and familiarity with building production services.
Experience implementing and productionizing AI frameworks with automated testing, monitoring, and data quality controls.
Strong architectural thinking and algorithmic intuition for model selection, evaluation, and optimization.
Ability to work independently in fast-moving environments and take ownership of complex technical challenges.

If you're ready for the next big step in your software development career, read on.
You'll work across the frontend and backend systems that form the backbone of the platform: responsive, scalable interfaces on one side, and reliable backend services and APIs on the other. Our stack is Python, React, TypeScript, FastAPI, and several more database types running on Kubernetes, with an emphasis on performant, secure, and reliable systems.
You'll work directly with product, design, and the rest of engineering, on a team small enough that you'll shape the architecture and the roadmap, not just execute against them.
Responsibilities:
Develop and maintain the core architecture of our AI agent platform, across both frontend and backend
Design and deploy scalable APIs, microservices, and backend systems in Python (FastAPI, Flask, or Django)
Build high-quality, responsive UIs in React and TypeScript: reusable components, optimized for performance and accessibility
Build with coding agents as a primary mode of work, and help define how our team does it well, including tooling, review standards, and the guardrails that keep quality high
Own features end to end, from architecture through production deployment. Yes, this means brand new pillars in the platform
Maintain high code quality through testing, code review, and clear standards, which matters even more given how much code we generate

As an AI Expert at our company, youll take full ownership of AI-powered product capabilities - from concept to production. Youll work closely with Product, Engineering, and Security domain experts to build practical, reliable AI systems that improve how teams operate and secure cloud environments. This is an applied role: we care about shipping, adoption, and measurable outcomes. Youll engage with users, test MVPs in production, iterate quickly based on feedback, and make sure what you build holds up in the real world. And yes youll also jump in as a Support Hero when needed, helping customers succeed and learning directly from how they work.
Key Responsibilities
Build and ship AI features that improve cloud security operations (e.g., control recommendations, drift detection explanations, guided remediation, policy generation, investigations, workflow automation).
Design and implement AI agents tuned for cloud-security tasks, including the surrounding framework (tools, permissions, orchestration, guardrails, and automation).
Apply deep understanding of cloud security domains (IAM, network controls, logging/telemetry, posture management, governance, misconfiguration, threat scenarios) to guide product decisions and ensure the AI behaves safely and correctly.
Integrate with multi-cloud APIs and security controls (AWS/Azure/GCP/OCI), building end-to-end flows from user intent → actionable outcome. Create and maintain evaluation methods for model quality (accuracy, coverage, hallucination rates, latency, cost, and user satisfaction) and build tooling to continuously measure performance in production.
Build data and feedback loops to keep systems relevant over time (user corrections, outcome tracking, regression testing, prompt/model iteration).
Stay current with practical advancements in LLMs/agents and engineering patterns, and adopt whats useful (not hype) into production.