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The Prisma Browser group is 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.
Prisma Browser is 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.

Responsibilities
Take full end-to-end ownership of features - from product thinking and design through development, testing, deployment, and post-production monitoring.
Collaborate with cross-functional teams to design, develop, and deploy high-quality web applications and services.
Champion and apply agentic development practices, leveraging AI-assisted tooling and automation to accelerate R&D velocity.
Develop robust and scalable systems using Node.js,C#,React, TypeScript and related frameworks.
Own the full DevOps lifecycle - CI/CD pipelines, containerization, release management, and production observability.
Optimize web applications for performance, scalability, and security across both microservice and monolithic architectures.
Write and maintain automated tests (unit, integration, end-to-end) to ensure code quality and reliability.
Utilize version control systems, particularly Git, for code management and collaboration.
Stay current with emerging technologies, AI tooling, and modern engineering trend.

We are seeking a skilled versatile software engineer with a vast experience in enterprise business applications development (like: Salesforce, NetSuite etc.) that will be part of the business applications development team and will also have a deep understanding of enterprise grade AI development and implementation, specifically, but not limited to, Microsoft 365 AI tools , Microsoft applications building tools, Claude tools and working with various AI models.
The ideal candidate will design, implement, and optimize business solutions and AI solutions that enhance Cellebrite business processes, productivity and collaboration across our organization.
Key Responsibilities:
Design, develop, and deploy business applications solutions and AI-driven solutions integrated with enterprise business systems.
Partner with IT analysts and business stakeholders to understand requirements, and translate them into scalable technical solutions, that deliver measurable business impact.
Build and integrate LLM-based solutions (e.g., OpenAI, Anthropic, AWS Bedrock), including coding, orchestration workflows using tools such as LangChain, n8n, or similar frameworks.
Own the end-to-end lifecycle of business applications and AI solutions -from prototyping and experimentation to production deployment, monitoring, maintenance & support and continuous optimization.
Integrate and enhance enterprise platforms (e.g., Salesforce, NetSuite, iPaaS services) with AI capabilities to improve business processes.
Evaluate, adopt, and standardize AI tools, frameworks, and best practices, while staying current with emerging technologies and proactively being part of a continuous innovation.
Maintenance and support - handling relevant tickets and bugs within defined SLA and customer satisfaction targets.

we are looking for an AI Specialist with deep, hands-on expertise in modern AI platforms to train, prototype, build and ship AI-powered solutions across the organization. Working closely with business stakeholders and IT peers , this role is a combination of an ability to design and work with AI consumers across the organization and technical - to turn the business needs into working AI solutions at speed using the latest agentic and low-code AI tools.
In this role you will also nurture the AI builder's community with advanced knowledge, technical sessions, builders CI/CD process support, creating business data foundations and literacy and responsible AI training and practices
Responsibilities:
Rapidly prototype and deliver working proof-of-concepts and working builds that translate business requirements into functional solutions, iterating quickly toward production.
Lead advanced, in-depth technical sessions, workshops, and hands-on labs for technical and semi-technical audiences, building internal capability in AI-assisted development.
Build and optimize agentic workflows, tool integrations, to maximize solution capability and reliability.
Establish and promote best practices for AI-assisted development, code quality, reusability, and responsible build standards across teams.
Drive build projects from concept through delivery, ensuring quality, performance, and maintainability.
Collaborate with Business Applications, IT R&D, Analytics and business units to identify high-impact opportunities and deliver measurable value through custom-built AI solutions.
This includes identification of required foundations to support the builders community (literacy, business models, RBAC foundations etc.)
Stay continuously current with the fast-moving AI development landscape - new models, platforms, agent frameworks, and tooling - and bring that knowledge into the organization.
Ensure compliance with corporate information security standards, data handling, and governance policies.

We are building a cutting-edge GenAI-powered SaaS platform that enables investigators to interact with complex case data through a conversational interface. The system leverages LLMs, RAG architectures, and agentic workflows in a rapidly evolving environment.
As we scale, we are looking for a hands-on R&D Manager, reporting to the VP R&D, to take ownership of day-to-day execution, team delivery, and production stability - enabling the team to move fast while freeing leadership to focus on new innovation and strategic initiatives.
This is not a traditional management role. You will operate as a player-coach, staying close to the technology while leading a small, high-impact team.
Key Responsibilities:
Lead the day-to-day execution of the development team (planning, prioritization, delivery)
Work closely with Product, DevOps, and Customer Success to ensure smooth delivery of features and fixes
Own production stability and ongoing support processes (triage, prioritization, resolution tracking)
Manage and mentor engineers while maintaining a flat, startup-like culture
Drive high engineering standards across code quality, testing, and system reliability
Stay hands-on with the system architecture, especially around GenAI pipelines, RAG, and agentic flows
Remove blockers and ensure the team can execute quickly in a fast-changing environment
Balance short-term delivery needs with longer-term technical health
Improve development processes without introducing unnecessary overhead

We are assembling an elite, small-scale team of innovators focused on transforming generative AI from breakthrough concepts into real-world products. As a Senior Data Engineer, you will serve as the data backbone of this GenAI innovation group, enabling rapid experimentation, research, and prototyping. In this role, you will transform complex, raw data into high-quality, AI-ready assets that directly power next generation of digital intelligence capabilities.
Key Responsibilities and Requirements:
Design, build, and maintain scalable data architectures that support generative AI research and rapid prototyping
Prepare, structure, optimize, and curate diverse datasets for AI and machine learning model training
Develop flexible, automated data pipelines to accelerate GenAI experimentation and development cycles
Partner closely with AI researchers and engineers to understand evolving data requirements
Conduct deep data exploration to uncover insights and identify new opportunities for GenAI applications
Ensure data quality, reliability, performance, and accessibility across multiple data domains
Optimize data processing workflows for large-scale and complex datasets
Apply strong data modeling and transformation practices to support advanced analytics and AI use cases

We are building the next generation of AI-powered investigator tools . Our cloud-based AI platform delivers cutting-edge capabilities including multimodal analysis, embeddings, OCR, speech-to-text, face detection, image and text classification, and LLM completions through a scalable serverless architecture.
As a Senior Software Engineer on the AI Platform team, you will be part of the core team that designs, builds, maintains, and evolves the shared AI service platform that powers real investigative workflows at enterprise scale. You will help turn advanced AI concepts into production-grade serverless systems, shaping orchestration, retrieval, context, and backend architecture across the platform.

We are seeking a Technical AI Project Manager to own and drive end-to-end internal AI initiatives and roadmap across our company, turning AI from experimentation into measurable productivity gains. You will be the single accountable owner for internal AI transformation solutions that fundamentally change how core processes work across IT, HR, Finance, Sales, and other business functions. You will lead initiatives where AI agents significantly automate end-to-end processes (e.g., recruitment, finance operations, internal IT workflows), partnering with our internal AI delivery teams and business stakeholders to move from idea to live, adopted, value-generating AI solutions.
How will you make an impact? 
You will own the full lifecycle of cross-functional AI initiatives: from opportunity framing through delivery, adoption, and measured outcomes. You will redesign business processes, translate business needs into actionable AI initiatives, and coordinate delivery with the IT AI Center of Excellence and business stakeholders.
This is a high-impact role for someone who combines strong business analysis with project leadership, thrives on driving concrete outcomes under ambiguity, and is energized by leading AI transformation across multiple business areas.
Project Ownership & Accountability
Own the full lifecycle of cross-functional AI initiatives, from opportunity framing through production rollout, adoption, and measured impact
Act as the single accountable owner for each AI transformation solution, with dedicated focus and clear lines of accountability
Drive concrete outcomes even when the solution is evolving or exploratory at the start; manage risks, dependencies, and stakeholder alignment proactively
Business Process Leadership
Analyze business processes deeply; identify where AI agents can automate, accelerate, or rethink existing work
Translate business needs into actionable AI initiatives with clear scope, success metrics, owners, and timelines
Partner with Finance, HR, IT, Sales, and other functions to redesign processes around agent-driven automation
Work hand-in-hand with the IT AI Center of Excellence
Outcomes & Impact
Partner with the AI analysts to define and report measurable impact: hours saved, cycle-time reduction, deflection, adoption, and ROI
Ensure every initiative reaches production with documented handover to operations and ongoing ownership.

We are seeking a highly skilled and experienced Senior AI and Analytics AML Product Manager to join our dynamic team. The successful candidate will play a crucial role in leading the development and execution of our AI and analytics AML products. This individual will work closely with cross-functional teams to define product requirements, drive product development, and ensure successful delivery to market. The Senior AI and Analytics Product Manager will leverage their expertise in machine learning, data analytics, and product management to drive innovation and deliver value to our customers.
This is a customer-facing role that requires regular interaction with financial institutions (FIs), gathering feedback, building relationships, and understanding the challenges and pain points. The role involves travel and deep engagement with customers to align our products with real-world needs. Knowledge of AML and financial crime is critical to succeed in this position.
How will you make an impact?
Product Strategy and Roadmap Development:
Lead the development of the AI and analytics AML products and roadmap, aligning with the company's overall vision and objectives.
Conduct market research and competitive analysis to identify market trends, customer needs, and opportunities for differentiation.
Define product priorities and initiatives based on customer feedback, business goals, and technical feasibility.
Product Planning and Execution:
Collaborate with cross-functional teams including engineering, data science, design, and marketing to define product requirements and specifications.
Drive the product development process from concept to launch, ensuring timely delivery and adherence to quality standards.
Manage the product lifecycle, including feature prioritization, release planning, and post-launch performance monitoring.
AI and Analytics Expertise:
Apply deep knowledge of machine learning techniques to enhance product capabilities and drive innovation.
Leverage data analytics and visualization tools to derive insights from large datasets and inform product decisions.
Stay abreast of advancements in AI technology, industry trends, and best practices to continually improve product offerings.
Customer Engagement and Feedback:
Work directly with customers, including banks and financial institutions, to understand their challenges and gather actionable feedback.
Present product concepts and solutions to customers, aligning product functionality with their needs.
Travel as needed to meet with customers, participate in industry events, and ensure products address real-world AML scenarios.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

As a Senior Data Science Researcher in our Workforce Management group, you will be part of a cross-sectional team working at the intersection of machine learning, GenAI, agentic AI, ML-Ops and Operations Research. You will develop the next generation of our companys Workforce Management solution for our customers contact centers - forecasting work volume, scheduling agent shifts, managing time-off requests, and owning the full lifecycle of the work schedule, in a high-stakes production environment serving many customers. Our solution has been recognized as the market leader for over 10 years.
How will you make an impact?
Developing end-to-end ML systems, tailored to specific project needs, to train and serve production-level models in a high-stakes environment.
Deploying GenAI and agentic AI systems to customers in production, improving on existing solutions or creating new services from scratch.
Designing and building agentic AI flows - tool use, multi-step reasoning, and orchestration.
Forecasting work volume and optimizing shift schedules using time-series methods and mathematical optimization.
Integrating new features and solutions into large existing software systems, stringing multiple disciplines together.
Running unstructured evaluation processes - finding noteworthy phenomena, experimenting, and feeding conclusions back to the team.
Owning projects end-to-end, leading team members and colleagues to efficient planning and delivery.