דרושים » תוכנה » AI Tech Lead - Agent Platform & Enablement

Own the intelligence layer - the AI research pipeline that classifies and risk-scores every tool we find. Discovery tells us what software an organization runs; the AI does the hard part: figuring out what each tool actually is, what it can do, how it handles data, and how risky it is. You'll own that research and enrichment system end to end - the LLM-backed agents, the prompts and context that drive them, the evals that keep them honest, and the cost and latency of running them at scale.

This is an engineering role, not a research one. You'll ship production TypeScript, and you'll be measured on the accuracy, cost, and reliability of the intelligence the product depends on.

What you'll work on

The multi-agent researcher system: LLM-backed agents that research each tool across topics like platform, data policy, AI models, and agentic capabilities, and return structured, evidence-backed classifications.

Evals and quality: design eval sets, measure classification accuracy and hallucination, and turn prompt changes into regression-tested, reviewable diffs instead of guesswork.

Grounding and trust: cite evidence, resolve contradictions between AI output and validated data, and drive down hallucination on the fields that matter.

Model routing and cost/latency: choose and route across providers, tune concurrency and caching, and keep the pipeline fast and affordable as volume grows.

Structured outputs, tool/function calling, and the schemas and validation that make model output safe to persist.

Deep observability into the pipeline - spans, traces, and metrics for every model call.

The company building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

We're hiring the engineering leader who will own its AI core.
Small senior team, greenfield architecture and real production environments from day one. You'll be one of the founding technical leaders - writing code, shaping the architecture, and growing the team as the product scales.
What You'll Do:
Agent Architecture & Engineering:
Design and build the AI systems at the center of the product: researcher agents, deterministic runners, multi-agent orchestration across thousands of targets and hundreds of sites.
Own the full agent stack - LLM selection and behavior, RAG pipelines, tool use, memory, evaluation, and observability. Make architectural decisions that will define how the product works for years.
Drive adoption of the modern agent ecosystem: LangGraph, MCP, semantic tool discovery, hybrid edge/cloud workflows, A2A patterns. Keep pushing the frontier.
Safety & Reliability:
Design for progressive autonomy: pre-checks, fault tolerance, rollback, and full audit trails. In our customers' environments - critical infrastructure, enterprise security - a wrong action has real consequences.
Build the evaluation and observability pipelines that make autonomous agent behavior trustworthy and debuggable in production.
Partner with Security and DevOps on agent execution boundaries, especially across on-prem ↔ cloud data flows.
Technical Leadership:
Spend most of your time in the codebase. Set the technical bar by example - architecture, code quality, and engineering judgment.
Establish standards for testing, evaluation, and safe deployment of AI systems. Build the practices that scale with the team.
Work directly with the PM and enterprise design partners to shape the roadmap. Your decisions will drive the product, not just execute it.
Team:
Start with a small senior group, grow it deliberately. Hire well, mentor, and shape the engineering culture of a startup inside a public company.

we are building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

We are seeking talented, motivated AI builders to join our Strategic Initiatives team as part of a new AI Center of Excellence. Reporting to the Sr. Director of Engineering, this team designs and ships AI-native solutions - agents, assistants, and reusable skills - that advance our security mission and multiply the impact of teams across the company.
This is a builder role. We're looking for experts engineers fluent in modern AI application development - agent frameworks, harnesses, tool use, prompting, retrieval, and evaluation - not machine learning engineers. You will be expected to train foundation models / frontier models . You will compose capable, production-grade AI systems on top of frontier models, get them into people's hands, and bring the rigor to prove they actually work.
What you'll do:
Design, build, and bring to production ready: models, AI agents, assistants, and reusable skills that solve real problems for us and our customers.
Turn ideas into working MVPs fast, then partner with engineering to harden them into reliable, production-ready systems.
Build evaluation into everything you ship - define what "good" means for each solution, measure it, and hold the bar. Due diligence through rigorous evals is non-negotiable, not an afterthought.
Compose agentic workflows that are secure by construction, using secure harnesses along with appropriate isolation, guardrails, and controls.
Select and integrate the right frameworks, tools, and models for each job, and turn what works into reusable patterns and best practices for the COE.
Also be a part of the COE Consult team to help across the organization.
Document what you build and how it should be used, operated, and evaluated.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

The same systems work as our senior role, aimed at someone earlier in their career who gets real work done in a hard codebase with agentic tools. Systems programming has a reputation for being locked behind ten years of experience. We don't fully buy that. Someone with solid fundamentals, real curiosity about how machines work, and deep control of agentic coding tools can contribute here much sooner - as long as they have the judgment to check what the tools hand back. That judgment matters more here than almost anywhere: the agent runs as root and SYSTEM, and the codebase forbids unwrap, panic, and unchecked indexing, so you can't ship code you don't actually understand.

What you'll work on

Real pieces of the sensor across macOS and Windows: inventory, telemetry, the policy enforcement path, installers, and the integrations that hook into AI coding tools and browser extensions.

Moving through unfamiliar OS APIs quickly with agentic tools, then checking the result against the docs, the tests, and a real machine.

Small tools and tests that make the codebase easier for everyone to work in.

Tech you'll work with

The work is low-level systems development in Rust, close to the operating system across Windows, macOS, and Linux - memory, processes, files, and the OS internals that expose them. Alongside that, you'll lean hard on agentic coding tools; deep fluency with at least one is central to how we work, not a bonus. Expect to range across the stack - OS internals, AI tooling internals, CI, and a fair amount of reverse engineering - and to learn the parts you don't know yet.

We're looking for a VP R&D to build both the technical foundation and the engineering organization behind that platform. This is not pure people management, and it's not a chief-architect role in disguise. It's a rare combination: a deeply technical leader who can challenge the strongest engineer in the room on architecture, and who also builds an organization where talented people grow, decide independently, and own outcomes end to end.



What You'll Own

Technical direction: Define architecture and technical strategy with the CTO, Product and AI leadership. Make real tradeoffs between speed, simplicity, reliability and long-term scale. Stay deep enough to lead architecture reviews, surface hidden risks, and tell the difference between an impressive demo and a dependable production system.
An enterprise-grade agentic platform: Agent orchestration, planning and tool use governed knowledge and policy representation retrieval, context and memory evaluations human review and escalation permissions, identity, security and auditability reliability and observability cost, latency and model performance enterprise integrations.
The R&D organization: Design the structure for the company's next stage. Recruit exceptional engineers and engineering leaders at a consistently high bar. Grow managers and senior ICs who own major domains independently. Set clear expectations, feedback and performance standards - and build a leadership bench so the org doesn't depend on a handful of people.
Autonomy with accountability: Define outcomes, decision boundaries and interfaces, then push decisions as close to the problem as possible. Autonomy that produces better and faster decisions - not ambiguity, duplicated work, or diffused responsibility.
Velocity and quality together: An operating model that moves fast without normalizing instability. Better planning, testing, deployment, observability and incident learning. AI-native development as real leverage for every engineer, with clear standards for security, correctness and human judgment.
Business impact: Shape the roadmap with Product rather than receiving requirements. Join strategic customer conversations where technical leadership builds trust and unblocks adoption. Operate as a company leader, not only a function leader.

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We develop the AI infrastructure that powers the AI experience throughout the product. We ensure the production AI platform is stable, reliable and secure.
Our mission is not only to build AI-powered features, but to enable every product team to build reliable, scalable, and high-quality AI experiences.
We work with modern LLM technologies, agentic workflows, MCP, retrieval systems, evaluation pipelines, distributed systems, and production-grade AI infrastructure, deploying to production multiple times a day.
Job responsibilities
What You'll Do
As a Tech Lead, you'll play a key role in shaping the future of AI .
You'll combine deep technical expertise with strategic thinking, helping multiple teams build production-ready AI experiences while driving engineering excellence across the organization.
In this role you will:
Lead the technical direction of one of AI domains.
Design scalable AI architectures and production-ready agentic systems.
Drive technical decisions around LLMs, Retrieval, Tool Calling, MCP, evaluation frameworks, and AI infrastructure.
Own complex cross-team initiatives from design through production.
Collaborate closely with Product, Data, Infrastructure, and Engineering teams to turn AI opportunities into customer value.
Help define engineering standards and best practices for building reliable AI systems.
Drive engineering excellence through architecture reviews, mentoring, and technical leadership.
Balance rapid experimentation with production-grade quality and scalability.
Evaluate emerging AI technologies and identify opportunities to improve our platform and customer experience.