לוח משרות דרושים AI Engineer, לחיפוש זה נמצאו 189 משרות

We are looking for a talented and motivated Software Engineer with hands-on experience building and operating multi-agent AI systems in production to join our company Automation Platform (DAP) team.
The team develops automation tools, orchestration capabilities, and intelligent platforms that simplify the deployment, management, troubleshooting, and optimization of large-scale network and AI infrastructure environments.
You will work on the design and development of AI-powered systems that bridge networking, automation, observability, and distributed infrastructure - running on Kubernetes at scale. Our stack includes LangGraph, Langfuse, RAG pipelines, MCP, and agent-to-agent (A2A) communication patterns.
This role combines strong software engineering with practical AI application development, with a sharp focus on production hardening, tracing, evaluation, and safety of agentic systems - not model training or research prototypes.

We're looking for an AI Specialist Engineer to join our AI Enablement team. This is a high-impact role where you will build products and internal tools used across the entire company, while working with the most advanced AI tools and best practices available today.
Key Responsibilities
Develop internal AI-powered products and tools used by teams across our company to work faster and smarter.
Lead the evaluation, integration, and rollout of AI-powered development tools across engineering teams.
Build proof-of-concept projects and internal tooling that demonstrate the practical value of AI in real workflows.
Develop best practices, prompt engineering guidelines, and training materials for AI-assisted development.
Stay current with the rapidly evolving AI tooling landscape and advise leadership on strategic adoption.
Work hands-on with the most advanced AI development tools and shape how the company uses them.

We're looking for an AI Workflow Engineer to join our System Architecture group. This is a high-impact role where you'll design, build, and continuously improve AI-powered workflows, agents, and tools that help our architects do their best work - from system design and modeling through documentation, technical decision-making, and review.
Working from within the architecture team, you'll turn the group's real, day-to-day pain points into practical AI-assisted solutions. Your primary focus is system architecture within the software development lifecycle (SDLC), and because architecture touches everything, your work will naturally extend into adjacent stages wherever AI can add value.
Key Responsibilities:
Design and build AI-powered workflows, agents, and internal tools that support system architecture work - design, modeling, documentation, architecture decisions, and reviews.
Develop and iterate on agents within our internal framework, using agentic AI techniques to accelerate and augment architecture and engineering tasks.
Continuously improve existing workflows and tools as team needs evolve and AI capabilities advance.
Partner closely with the System Architecture team to understand real pain points and turn them into practical, AI-assisted solutions.
Measure and demonstrate the impact of the workflows you build - for example on design-cycle time, review turnaround, and the quality and completeness of architecture documentation and decision records.
Build proof-of-concept projects that show the practical value of AI in real architecture and engineering workflows.
Develop best practices, prompt engineering guidelines, and reusable patterns for AI-assisted architecture and development.
Stay current with the rapidly evolving AI tooling landscape and advise on strategic adoption.

Join us to build cutting-edge systems, collaborate with world-class engineers, and shape how autonomous software is built in production.

We are looking for an AI Engineer to join the hunt.

Responsibilities:
Take part in the design and development of AI-driven features for our Dev-Native Observability Platform.
Collaborate with cross-functional teams to integrate AI solutions into existing systems.
Contribute to the product roadmap by identifying AI opportunities and providing insights as a potential end user
Stay updated with the latest AI trends and technologies to ensure our platform remains cutting-edge.

We are looking for a Senior AI Engineer to design and build the intelligence layer.
You will own the agentic workflows, LLM integrations, and reasning pipelines that allow our AI agents to autonomously analyze markets, make decisions, and drive eCom growth at scale.
What you'll do:
Design & Build Agentic Workflows: Architect multi-agent pipelines - including planning, memory, tool use, and decision loops - that power autonomous media buying and growth operations.
Own LLM Integration: Select, prompt-engineer, fine-tune, and evaluate LLMs to produce reliable, high-quality outputs across diverse business tasks.
Build RAG Systems: Develop retrieval-augmented generation pipelines with vector search and context management to ground agent reasoning in real business data.
Drive Evaluation & Reliability: Define evals, build testing frameworks, and continuously improve agent output quality, consistency, and safety in production.
Collaborate Across the Stack: Work closely with backend engineers to integrate AI capabilities into core product APIs, ensuring low-latency, production-grade deployment.

Were looking for an experienced AI Team Lead to build and lead this team - someone who combines strong engineering leadership with hands-on experience building production AI systems and platforms.

What youll do
Build and lead AI dedicated team from the ground up.

Define, implement and maintain the company-wide AI platform, infrastructure, and developer tools.

Enable R&D teams to build, test, deploy, and monitor AI-powered capabilities independently.

Lead the design and delivery of shared AI experiences and services across the company.

Establish standards for quality, reliability, security, observability, and cost management.

Partner with Product, Engineering, DevOps, Security Research, and other teams to identify high-impact opportunities and turn them into scalable solutions.

Stay hands-on with architecture, implementation, production issues, and technical decisions.

Mentor engineers and raise the engineering bar for AI development across R&D.

we are a leading creative technology company on a mission to empower creators and brands to bring their vision to life with video. Offering cutting-edge AI tools and models for image, video, and voiceover creation, alongside high-quality creative assets and powerful editing tools, our company enables creators to stay on trend and achieve their creative goals. Trusted by over 30 million creators worldwide and top brands including Google, Amazon, Microsoft, and Versace, our company provides a seamless, subscription-based platform with a global license, giving creators everything they need to produce professional video content efficiently. we are looking for an AI Enterprise Solution Expert to drive strategic AI sales across enterprise brands, media, and advertising. Working closely with sales leadership, this role will deliver hands-on AI expertise, custom-built solutions, proof of concepts, and Embedded creative services that enable brands to produce consistent, production-ready AI content at scale. Responsibilities
* Serve as the primary AI sales expert for a portfolio of existing and potential enterprise accounts, providing Embedded, high-touch engagement across pre and post sales, workshops, office hours, and ongoing advisory
* Guide clients through end-to-end AI content production, applying deep knowledge of pre-production, production, and post-production to ensure quality and consistency at scale
* Co-develop SOWs and build POCs with enterprise clients to move engagements from concept to execution
* Create high-impact AI product case studies and showcase projects
* Provide ongoing creative and Technical Support for strategic clients, helping transform initial demonstrations into successful long-term collaborations
* Translate product capabilities into compelling creative stories and business opportunities
* Support flagship initiatives and future showcase productions

Were looking for a Founding AI Engineer to build and define the research function at Upriver from zero. This is a rare opportunity to work on some of the hardest unsolved problems in applied AI and agents, with massive ownership, visibility, and room to grow into leadership quickly.

What Youll Do
Lead the AI domain at Upriver: Take ownership of our agent architecture and help define its next phases, standards, and direction.
Solve hard, unsolved problems: Work on challenges in AI agents, reasoning over complex systems, and autonomous decision-making that dont yet have clear playbooks.
Build production AI: Design, implement, evaluate, and deploy AI systems used by real customers.
Be outward-facing: Publish technical blogs, benchmarks, and papers; represent Upriver in the AI and data community.
Move fast with autonomy: Own problems end-to-end with minimal guidance, working directly with founders.
Deliver real customer value end-to-end: take features from idea to production, see them used by customers, and iterate based on real-world impact.

Required AI Algorithm Engineer - Vision Localization Group
About the team:
Our vision Localization group is responsible for accurately localizing the vehicle on a high-definition 3D map with centimeter-level precision using surround camera systems to enable autonomous driving. Our work combines advanced geometric reasoning, scene understanding, and large-scale mapping to solve one of the core challenges of autonomous vehicles in complex real-world environments.
The role involves developing algorithmic and deep learning solutions that integrate mathematical optimization, computer vision, and 3D geometry. We work with modern neural network architectures including Transformers, GNNs, and CNNs to solve problems involving scene understanding, object association, and precise spatial reasoning in dynamic environments. The HD map contains rich geometric and semantic information that is critical for safe driving and cannot always be inferred reliably from images alone under all visibility conditions or viewpoints. In addition to achieving highly accurate localization, the team is also responsible for estimating map relevance and consistency with the current world state to ensure safe and reliable autonomous driving.
What will your job look like:
Develop state-of-the-art localization algorithms for autonomous driving using multi-camera surround vision systems.
Design and implement deep learning models based on Transformers, Graph Neural Networks, and Convolutional Neural Networks.
Solve challenging problems involving 3D geometry, scene understanding, object matching, and spatial reasoning.
Combine learning-based methods with classical optimization and geometric algorithms.
Own the entire lifecycle from prototyping to deployment.
Analyze real-world driving data and improve system robustness under diverse environmental and visibility conditions.
Drive innovative solutions for high-precision localization at production scale.

As an AI Engineer, your primary focus will be delivering working features while adopting a Builder Mindset to shape the future of software development. You will leverage cutting-edge GenAI tools (such as Claude Code and others) to accelerate development, streamline workflows, and bring high-quality, production-ready features to life.
In this role, you will work closely with PMs, designers, and fellow engineers across frontend, backend, and data domains. You will use AI to tackle complex debugging, conduct research, and eliminate repetitive tasks-continuously improving your productivity while delivering functional, impactful features within your pod.
Responsibilities:
Feature Delivery: Own and deliver working features from end to end, contributing production-ready code with guidance and driving tangible product value.
AI-Accelerated Development: Utilize GenAI tools (e.g., Claude Code) to accelerate feature development, improve personal productivity, and optimize daily workflows.
Problem Solving & Debugging: Leverage AI for rapid debugging, technical research, and reducing repetitive manual work during feature delivery.
Cross-Domain Collaboration: Build a solid understanding across product, frontend, backend, and data domains, collaborating effectively with PMs, designers, and engineers.
Context & Documentation: Document technical decisions clearly and contribute to building shared team context and knowledge bases.
Continuous Learning & Adaptability: Learn the tech stack quickly, adapt smoothly to new tools/workflows, and actively apply feedback to grow as a builder.
Pod Reliability: Act as a dependable, collaborative contributor within your pod, helping drive team momentum and execution.

This role owns the AI-native GTM engine: the systems that find, enrich, reach, and route prospects and customers, and the workflows that create new opportunities for the company. You build revenue systems with AI tooling, then run them against pipeline targets. The scope is wide on purpose. You own the motion and the measurement, so theres no gap between what you build and what you can prove.

What youll own
Outbound engine. Multi-channel outbound end-to-end: list building, segmentation, messaging, sequencing, launch, and A/B iteration across email and LinkedIn. AI-driven personalization at scale, moving prospects from first touch to booked meeting with minimal manual input from sales.
Inbound conversion. Everything between the first visit and the booked meeting: on-site conversion paths, forms, chat, scoring, routing, and speed-to-lead. Instrument every step, find where intent leaks, and close the gap with automation instead of headcount. No inbound lead should sit waiting for a human to qualify it.
CRM truth and attribution. The measurement layer under everything else. If outbound, inbound, AEO, and content cant be attributed, none of it can be optimized or defended. Own data quality across HubSpot and Salesforce.
Data and enrichment. Prospect identification and signal tracking: enrichment waterfalls, buying signals, social listening, and clean push architecture into the CRM.
AI agents and automation. Claude is a teammate here, not a chatbot. Build the agents, skills, and workflows that encode our GTM playbooks and kill anything manual.
AI content and discovery. Build the writing agents that produce inbound at volume: programmatic pages, competitor comparisons, persona and vertical landing pages, localized variants, and one asset turned into fifteen across channels. AEO is the other half of this. Buyers now start in ChatGPT, Claude, Perplexity, and AI Overviews, not on a results page, so the content has to be built to be retrieved and cited: structured content and schema, presence on the review sites and communities models pull from, and a technical layer that makes our legible to crawlers.
Customer expansion. The signal engine that finds revenue inside the base: segmentation, expansion triggers, and routing each account to the right owner or the right sequence at the right time.
The stack itself. The GTM tech stack end-to-end: evaluate, buy, integrate, and kill tools as the motion evolves, and build tools with direct impact on revenue when nothing off the shelf does the job.

we are looking for a AI Engineer.
You will build the shared platform layer that makes agentic AI extensible, measurable, and self-improving. This is the foundation beneath everything: the orchestration engine, the skills registry, the evaluation harness, the guardrails system, the memory service, the integration gateway, and the tracing infrastructure - consumed by both the real-time conversational and offline video generation runtimes.
Your work is what separates it works for one customer with engineering involvement from it works for many customers across many domains without bespoke engineering per customer. You build the machinery once; customer-facing teams and eventually customers themselves extend it.

we are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.

We are looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables our algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow us to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.
As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.
What will your job look like?
Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for our developers.
Expand the capabilities of existing platforms by embedding smart, context-aware LLM features.
Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them.
Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG).
Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves.

We are looking for a Hands-On Agentic AI Engineer (Information Systems Team) to join a new team focused on applying AI agents and intelligent workflows to improve business processes across the organization.

The ideal candidate is someone who enjoys building real-world AI-driven solutions end-to-end - writing code, designing architectures, building prototypes, solving implementation challenges, and working directly with business stakeholders to turn process opportunities into working AI solutions.



Responsibilities:

Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots)
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners and IS teams to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations