לוח משרות דרושים AI Engineer משרה מלאה, לחיפוש זה נמצאו 179 משרות

The Cortex Content team powers Palo Alto Networks security operations platforms (XSIAM/XSOAR) by building the essential integrations, data connectors, and intelligence layers that protect our customers.
We are looking for a Senior Software Engineer who is passionate about AI and automation to help scale our content ecosystem. In this role, your focus will be leveraging AI to accelerate the delivery, quality, and quantity of our security integrations and content infrastructure. You will design and develop intelligent tools and workflows that enable engineering peers and Technical Program Managers (TPMs) to scale their output, collaborating with them as they test, adopt, and integrate these solutions into our development lifecycle.
Your Impact:
Scale Content Delivery: Design and implement AI-driven systems that empower the team to significantly increase both the quantity and the rigorous quality of our security integrations.
Develop Cross-Functional Tooling: Build internal developer tools and agentic workflows that enable other engineers and TPMs to streamline content creation, validation, and management.
Optimize Workflows: Create reliable CI/CD pipelines incorporating AI agents to automate code generation and testing processes.
Support Team Adoption: Collaborate with engineering peers and cross-functional partners as they test and integrate your tools into their daily workflows, using their feedback to drive continuous improvements.
Contribute to AI Integration: Help lead the practical integration of LLMs and automation frameworks into our day-to-day engineering processes.

Your Career:
As a Principal AI Software Engineer on our Generative AI foundation team, you will build AI agents and shared infrastructure that power GenAI capabilities across the company. You will be involved in the full software lifecycle, from fast-moving prototypes to production-grade systems, enabling our companys product lines to adopt agentic workflows safely and at scale. You will collaborate with product managers, researchers, and engineers to translate ideas into robust services, bringing a pragmatic mindset to experimentation, iteration, and operational excellence.
Your Impact:
Engage in the full software lifecycle, from fast-moving prototypes and POCs to production-grade, observable systems.
Collaborate with product managers, researchers, and engineers to translate ideas into robust services.
Work hands-on with leading LLMs and the modern agentic ecosystem, leveraging a wide range of AWS services.
Use cutting-edge frameworks and techniques to develop, evaluate, and continuously improve solutions.
Implement solid design, flexible implementation, strong telemetry, and production feedback loops.

As a Senior Staff AI Software Engineer on our Generative AI foundation team, you will build AI agents and shared infrastructure that power GenAI capabilities across the company. You will be involved in the full software lifecycle, from fast-moving prototypes to production-grade systems, enabling our companys product lines to adopt agentic workflows safely and at scale. You will collaborate with product managers, researchers, and engineers to translate ideas into robust services, bringing a pragmatic mindset to experimentation, iteration, and operational excellence.
Your Impact:
Engage in the full software lifecycle, from fast-moving prototypes and POCs to production-grade, observable systems.
Collaborate with product managers, researchers, and engineers to translate ideas into robust services.
Work hands-on with leading LLMs and the modern agentic ecosystem, leveraging a wide range of AWS services.
Use cutting-edge frameworks and techniques to develop, evaluate, and continuously improve solutions.
Implement solid design, flexible implementation, strong telemetry, and production feedback loops.

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness speed and scale in the frameworks they already use.

We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with as a vector store, ML feature stores, and LLM-powered data applications.

columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do
Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
Drive the AI/LLM integration strategy: designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

Who We’re Looking For – The Team Lead & Architect Chargeflow is at the forefront of fintech and AI innovation. Our mission is to build a fraud-free global commerce ecosystem by leveraging cutting-edge technology—freeing online businesses to focus on their core ideas and growth. We’re looking for an experienced, hands-on AI Platform Engineering Team Lead to lead the team building the AI agents, intelligent workflows, and decision-making systems powering Chargeflow’s products. This role combines technical and architectural leadership with people leadership . You’ll lead engineers, build alongside them, and own production systems that must operate reliably, securely, and at scale.
This Is Not Your Typical Leadership Role If you’re looking to manage tasks, run standups, and stay above the code—this position probably isn’t for you. But if you want to build AI systems, shape engineers, and redefine how software teams operate in the age of AI—keep reading.
What We’re Actually Building At Chargeflow, we’re building systems that don’t just process data—they understand context, make decisions, and take action. Real-time. At scale. Under uncertainty. Our AI Platform brings together models, data, tools, business logic, and intelligent workflows to solve complex problems across the chargeback and fraud lifecycle. This isn’t a pure infrastructure or AI research role—and it isn’t about building impressive demos. It’s about turning rapidly evolving AI capabilities into reliable, production-grade products with measurable business impact.
What You’ll Actually Do
* Lead a team of strong software engineers—and build alongside them
* Own the architecture and evolution of Chargeflow’s AI Platform
* Build production-grade AI agents, intelligent workflows, and decision-making systems
* Combine LLMs with business logic, data, tools, and deterministic software
* Own AI systems across their lifecycle—from design and implementation to deployment, evaluation, and continuous improvement
* Make architectural trade-offs across accuracy, latency, scalability, reliability, security, and cost
* Turn ambiguous product and business problems into clear technical direction
* Raise the bar for code quality, system design, and engineering execution
* Help strong engineers become exceptional

We're looking for a Senior AI Solutions Builder to join our Product team. In this role, you will design, build, and ship AI-powered solutions that enhance productivity and drive measurable business value across the organization.

You will operate at the intersection of AI, data, pricing and business - owning the end-to-end development of agents, copilots, and workflow automations that directly inform decisions and eliminate manual work.

You are both a builder and an enabler. You ship solutions yourself and raise the AI capability floor for the teams around you. You'll collaborate closely with Product, Engineering, CS, Sales, and builders across the company to deliver outcomes that improve decision velocity, product and operational efficiency, and long-term growth.

You Will

Design and ship AI-powered copilots, agents, and decision-support tools for the Product team, starting with Pricing.

Build data pipelines and analytical models for pricing intelligence.

Develop autonomous agents that automate recurring Product tasks.

Automate manual workflows across product tools so the right action happens when a signal fires.

Build LLM-based scoring models that give roadmap and prioritization decisions a data backbone.

Enable and upskill product teams through AI playbooks, sessions, and hands-on co-building.

Collaborate with builders across Engineering, CS, GTM, and other teams to co-create solutions that scale.

Define and track impact: time-to-insight, decision velocity, manual hours eliminated, and business outcomes.

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 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.

As an AI Product Owner & AI Process and Agentic Designer, you will own the process of transforming complex workflows into scalable AI solutions, bridging the gap between business stakeholders and AI Engineers. You will lead business process analysis, identify agentic process opportunities, validate business value, and define solution designs that enable successful AI implementation across the organization.

Responsibilities

Business Process Analysis & Discovery
Partner with business stakeholders to map, document, and analyze existing workflows, identifying inefficiencies, bottlenecks, and manual processes ripe for automation.
Conduct discovery sessions and workshops to deeply understand business needs, pain points, and success criteria across departments.
Agentic Opportunity Identification
Evaluate workflows to pinpoint where agentic and AI-driven approaches can deliver the most value, distinguishing tasks suited to automation, augmentation, or full agentic orchestration.
Maintain a prioritized pipeline of AI/agentic opportunities, assessing each for feasibility, complexity, and impact.
Business Value Validation
Define and quantify the expected business value of proposed solutions (e.g., cost savings, time reduction, quality improvement, revenue impact) and build the business case for investment.
Establish success metrics and KPIs, and track realized value post-implementation to ensure outcomes match projections.
Solution Design & Definition
Translate business requirements into clear solution designs, including agentic process flows, decision logic, data inputs/outputs, integration points, and human-in-the-loop checkpoints.
Define guardrails, escalation paths, and fallback behaviors to ensure solutions operate safely and reliably.
Stakeholder & Engineering Bridging
Serve as the primary liaison between business stakeholders and AI engineers, translating business intent into technical requirements and engineering constraints back into business terms.
Own the product backlog, prioritize features, and clearly communicate scope, trade-offs, and timelines to all parties.
Implementation Enablement & Iteration
Guide solutions through the full lifecycle from concept to deployment, supporting testing, validation, and change management.
Gather feedback, monitor performance, and drive continuous refinement of deployed AI and agentic solutions.
Scalability & Standardization
Identify reusable patterns, components, and best practices to scale successful solutions across the organization.
Contribute to governance frameworks, documentation standards, and design principles for AI/agentic product development.

we are seeking an experienced and visionary ML Engineer to join our dynamic technology organization. The successful candidate will be a part of a team of talented AI Engineers, Building agentic AI solutions, driving innovation and delivering business value through advanced Generative AI solutions & machine learning techniques. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge Gen AI and LLM methodologies and a passion for continuous learning and development.

Responsibilities

Build the solution: Own end-to-end technical delivery of agentic systems, from source-system integration through agent design, development, evaluation, and production deployment.
Integrate AI systems with source systems (Salesforce, Databricks, Splunk, internal APIs, business applications). Handle agent harness and orchestration.
Handle the operational handover to the business function and any necessary support transition.
Collaborate across teams: Partner closely with data engineering, platform, security, and business teams to align on requirements, dependencies, and integration points.
Engage stakeholders: Gather requirements directly from business functions, communicate technical trade-offs clearly, and keep stakeholders informed on progress, risks, and timelines.
Uphold quality and reliability: Establish and maintain best practices for code quality, testing, evaluation, monitoring, and observability of deployed AI systems.
Contribute to the team's technical growth through knowledge-sharing and help shape engineering standards and reusable patterns.
Stay current: Continuously evaluate emerging Gen AI, LLM, and agentic frameworks, and recommend tools and approaches that improve delivery speed and solution quality.

We are looking for a Principal Tech Lead to join our team.

So, what will you be doing all day?

As a Principal Tech Lead, your daily impact spans deep-tech execution, organizational mentorship, and bleeding-edge paradigm adoption:

Architectural Governance: Own the end-to-end design, scalability, and performance of complex backend distributed systems. Ensure architectures are resilient, modular, and optimized for intensive data workloads.
Agentic Engineering Innovation: Champion and embed Agentic SDLC paradigms, tools and solutions into the engineering lifecycle. Design frameworks that enable autonomous AI agents to build, test, and deploy features end-to-end, while enforcing rigorous quality gates and deterministic production guardrails.
Cross-Project Orchestration: Maintain an exceptionally large context window to actively guide and facilitate multiple highly technical streams and parallel projects simultaneously without losing focus or delivery speed.
Guild Leadership & Mentorship: Act as the primary technical sounding board for the engineering community. Lead backend guild sessions, introduce emerging technologies, and actively mentor senior and staff engineers to elevate the group's collective capabilities.
High-Velocity Execution: Lead by example. When taking on critical path, hands-on tasks, execute with unmatched speed and quality, demonstrating what peak operational efficiency looks like.

As an AI Engineer , you will be a key player in building our new AI platform, AI Studio. This platform aims to provide users with instant answers to complex market research questions, generate world-class reports, and create live, interactive, and powerful dashboards. Our goal is to reduce the complexity and time-to-insight for our users, helping them streamline their market tasks and achieve automation that drives real value.

So, what will you be doing all day?

Design and develop LLM-based intelligent agents that can handle complex market research queries.
Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
Collaborate with product, data, and UX teams to ensure that AI solutions are aligned with user needs and business goals.
Continuously evaluate and improve the performance of AI models to ensure high-quality and data-driven results.

This is not your typical leadership role.
If youre looking to manage tasks, run standups, and stay above the code - this position probable isnt for you.
But if you want to build systems, shape engineers, and redefine how teams operate in the age of AI - keep reading.
What were actually building
were building systems that dont just process data - they make decisions.
Real-time. At scale. Under uncertainty.
Fraud evolves fast.
So our systems, and our engineers, need to evolve faster.
This is where you come in.
What youll actually do:
Youll lead a team - but youll also build with them
Youll make architectural decisions that directly impact how the system scales and behaves
Youll own critical parts of the system - not just people
Youll turn ambiguity into direction (for both product and engineering)
Youll raise the bar: in code quality, system design, and how the team thinks
Youll embed AI into how the team works - not as a tool, but as leverage
Youll help strong engineers become exceptional

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.