דרושים » AI » AI Lead (Prisma Browser - Agents Fleets)

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 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.

Required AI Platform Engineering Team Lead - Sovereign AI Engineering
We're building AI that nations own and control, deployed where almost no one else can operate. ingesting and structuring complex data, and driving practical actions that can literally impact the lives of billions of people around the world. This role helps make that real.
The Dream Job:
It starts with you - a technical leader driven to build both the agentic AI platform and the engineering team behind it. You care about backend quality, platform reliability, and growing engineers through real ownership. We are AI-first across the board - every team builds and operates agents. You'll set the technical direction for the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments. You stay close enough to the codebase to debug production incidents, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the agentic AI platform driving Sovereign AI products - this role is for you.
Responsibilities
Architect and evolve the AI platform - agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines - through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure - hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services - set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices - agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group - ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as system performance.

We're looking for an Engineering Group Manager to lead this group. Reporting to the VP of R&D, you'll own the engineering execution, architecture, and technical direction of the products that define what Optibus is. You'll lead a group of full-stack team leads and engineers building large-scale, complex SaaS products on a cloud-native platform, working closely with our Algorithms group.

This is not a 2024 group manager role. We are not hiring someone whose day is meetings, status reports, and approvals. We are hiring a hands-on technical leader who codes, designs systems, drives architecture, leverages AI as a force multiplier, and is measured by impact rather than headcount.

What You'll Own

R&D leadership partnership. You're a key member of the R&D leadership team, actively shaping departmental strategy and driving high-level decisions across the organization - not just running your own group.
The core of the product. Engineering ownership of Planning, Scheduling, and Rostering - the products at the center of the platform, used by transit agencies and operators worldwide.
Architecture and technical direction. You set the technical bar. You participate in design, review architecture, write PRDs, and dive into code reviews when it matters. The hardest problems land on your desk.
A group of team leads and engineers. You'll mentor team leads, grow engineers, and build a small, sharp, high-leverage organization rather than a large one.
Delivery, quality, and velocity. You drive the engineering processes that ship a high-quality product on production-grade SaaS - fast, with low defect rates, and with continuous improvement.
AI-native engineering. You leverage AI agents and automation to multiply the output of your group - both in how the team builds software and in what we build into the product.
Cross-functional partnership. Tight collaboration with Product, Design, Algorithms, and Customer-facing teams to translate complex transit-domain needs into shipping software.

we are looking for a AI Engineer Team Lead
What will you do?

Own the agentic infrastructure- You're responsible for the architecture of Klaudia's core - the systems that make autonomous investigation, root cause analysis, and self-healing possible at scale.

Build and grow your team- Hire great engineers, mentor them, run meaningful design reviews, and build a culture of high ownership and engineering excellence.

Drive experimental development- Run structured bets - LLM evaluation frameworks, new agent architectures, novel tool-use patterns - and bring the best ones to production.

Stay hands-on You'll design systems, review PRs critically, and write code where it matters. Your technical credibility is the foundation of your leadership.

Shape our AI strategy- Work closely with product and R&D leadership to define what Klaudia becomes next.

We're hiring a Mobile Team Lead to own the AppHarbr SDK, GeoEdge's mobile division that gives publishers real-time control over ad quality across iOS and Android. About AppHarbr AppHarbr is GeoEdge's mobile division, a lightweight SDK that integrates directly into apps and games, giving publishers real-time control over ad quality. It proactively blocks disruptive, intrusive, and malicious ads before they ever reach users. Trusted by industry leaders, AppHarbr runs at scale across some of the world's most widely used mobile apps, sitting at the intersection of mobile performance and ad security. The role We are looking for an experienced Mobile Team Lead to take full ownership of the AppHarbr SDK across iOS and Android. This is a hands-on technical leadership role, you will spend the majority of your time making architectural decisions, setting technical direction, and driving cross-functional execution across a team that includes offshore contributors, while writing and reviewing code. This is a team that moves with the tools available to it, AI-assisted development is part of how we work, not a future aspiration. The SDK runs inside production apps across millions of devices, operating in real time alongside a continuously evolving ecosystem of third-party mediation layers and ad networks. Reliability, backward compatibility, and runtime performance are baseline expectations, not aspirational goals. Responsibilities Technical
* Architect and develop high-performance, multi-threaded SDK code across iOS and Android
* Drive platform technical decisions, performance optimizations, and deep research into OS internals and third-party SDK behavior
* Own SDK stability across the full compatibility matrix, OS versions, devices, and mediation integrations
* Lead complex debugging efforts, reverse-engineer third-party integrations, and resolve integration issues for internal and external stakeholders
* Define and enforce best practices in testing, code quality, and CI/CD workflows
* Handle billions of impressions across millions of devices every day, maintaining a robust and scalable system at significant traffic scale
* Leverage AI-assisted development tools to accelerate code generation, debugging, and SDK analysis, and set the standard for how the team adopts them effectively Leadership
* Lead, mentor, and grow a cross-platform mobile team including offshore contributors
* Own the AppHarbr domain end-to-end, translate product and business goals into technical execution
* Act as the primary technical point of contact across Product, Backend, QA, Customer Success, and external partners
* Conduct code reviews and provide ongoing technical guidance to raise the bar across the team Requirements
* Proven hands-on experience shipping production code on both Android and iOS
* 5+ years of professional mobile development, with 2+ years in a formal Team Lead or engineering management role, with proven people management experience
* Deep understanding of OS internals, memory management, and multi-threaded architectures on both platforms
* Strong ownership mindset, able to operate independently and drive initiatives from inception to production
* Excellent debugging skills, with a modern toolset that includes AI where it adds real value Strong advantages
* Experience building or leading teams building mobile SDKs
* Experience managing remote or offshore engineering teams
* Background in adtech, mobile monetization, or ad quality
* Familiarity with Unity integrations and ad mediation frameworks
* Broad product and business perspective, able to coordinate across multiple stakeholders inside and outside the organization Working at GeoEdge GeoEdge is the global leader in ad security and quality, trusted by hundreds of the world's top publishers, ad platforms, and app developers. We are a focused team that cares about craft and impact. You will work on a pr

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 looking for a Technical Lead to drive the architectural direction and engineering excellence of this group. This is a senior, deeply hands-on role for a technology leader who can own the technical roadmap, mentor a team of elite engineers, and build the infrastructure that challenges platform to its theoretical limits.
What You'll Lead:
Define and own the technical architecture of the group's distributed testing and reliability platform - designing for massive scale, real-world workload simulation, and adversarial failure injection
Lead effort involving multiple engineers, setting technical standards, running architecture reviews, driving design decisions, and mentoring engineers to grow
Build the systems that orchestrate millions of concurrent IO operations, inject chaos at the infrastructure layer (latency, packet loss, hardware failures), and expose the hardest-to-find race conditions and consistency bugs
Advance AI-driven approaches to test automation: intelligent scenario generation, LLM-augmented root-cause analysis, and autonomous validation pipelines
Drive observability and reliability engineering across the group - building telemetry pipelines that track P99 latency, jitter, and system health, turning quality into a quantitative discipline
Collaborate deeply with Core R&D, Storage Kernel, and Infrastructure teams - translating architectural knowledge into targeted reliability strategies
Establish engineering practices - design docs, production-grade code reviews, testing philosophy, and cross-team technical alignment

We are looking for a Staff AI Platform Engineer to join the Development Platform team, which is responsible for accelerating our R&D velocity and elevating product quality.
We need a pragmatic systems thinker who is deeply passionate about Developer Tools (DevTools) and the future of generative AI. You can architect a distributed LLM workflow in the morning, and influence engineering behavior across the entire org in the afternoon.
In this role, you will enable 200+ engineers to safely and reliably leverage agentic AI.
What You'll Do:
Scale Quality, Not Just Code Volume: Solve the hard problem of AI-driven quality by building rigorous measurement frameworks, ensuring that an AI-accelerated org delivers better software, not just more software.
Establish AI Governance & Security: Architect guardrails around LLM usage, data privacy, and cost management, ensuring 200+ engineers can innovate rapidly without compromising compliance or security.
Optimize the SDLC: Embed autonomous workflows, coding agents, and tool/context design directly into the developer lifecycle to supercharge R&D velocity.
Drive Org-Wide Adoption: Act as a force multiplier, leading the organizational rollout of AI developer tooling and setting production best practices across teams.
Build Agentic Infrastructure: Design and deploy production-grade multi-agent systems, tackling complex challenges like parallel session isolation, subagent delegation, and self-improving feedback loops.

Required Senior AI Platform Engineer - Sovereign AI Engineering
The Dream Job
It starts with you - an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. We are AI-first across the board - every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products - this role is for you.
Responsibilities
Design and build agentic systems - single and multi-agent workflows with planning, memory, context engineering, and tool use - for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer - LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization - deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer - orchestration primitives, execution environments, state management, and sandboxed tool execution - giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior - automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs - with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems - indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack - model routing, caching, batching, and inference cost management.
Ship shared tooling - libraries, SDKs, agent templates, and documentation - while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.