דרושים » AI » AI Engineering Lead

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.

Key responsibilities

Lead a team of data scientists, data engineers and product analysts

Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation

Drive excellence, code quality, and best development practices

Provide technical direction and hands-on guidance for complex AI systems

Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences

Ensure AI solutions are safe, scalable, observable, and continuously improving

Lead initiatives around product data analysis and experimentation

Analyze user interactions with product features to improve accuracy, UX, and business impact

Partner with product teams to define metrics, dashboards, and experiments that guide product improvements

Design system architectures for AI-enabled applications at scale

Evaluate and select technologies for AI platforms and data pipelines

Guide the development of prompt engineering frameworks and centralized prompt management

Ensure robust monitoring, evaluation, and feedback loops for AI outputs

Translate product and business goals into technical roadmaps and execution plans

Drive alignment between AI capabilities and measurable product outcomes

A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.

What You'll Do

Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.

We are looking for a Senior AI Ops Engineer to join our AIOps team and help build the AI-powered platforms, agents, and workflows that enable engineering teams across our company.
This is a hands-on role for someone who can take AI solutions from idea to production, build the infrastructure around them, and create the observability needed to understand and improve how they behave in the real world.
Job responsibilities
Build and operate production-grade AI agents, AI-enabled tools, and workflows that empower developers and operational teams.
Design agentic systems using frameworks such as LangGraph, LangChain, and Supervisor, including RAG, MCP integrations, tool calling, and guardrails.
Build and evolve an AI SDLC harness that helps teams plan, implement, validate, govern, and safely deliver AI-assisted software changes.
Lead AI observability with Langfuse and OpenTelemetry, including tracing, evaluations, quality measurement, and cost analysis.
Build automated evaluation frameworks, including LLM-as-a-judge, to improve the reliability and quality of AI systems.
Design and maintain the infrastructure, CI/CD pipelines, GitOps workflows, and security controls that support AI services.
Develop internal developer tooling and automation that removes friction across the engineering organization.
Partner with engineering and product teams to turn real operational challenges into scalable AI-powered solutions.

We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.

You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.

The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.

What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

We're looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions for our Product Support organization.
This is not a research role. You'll design and ship real AI systems that improve support workflows and processes - building custom solutions where off-the-shelf tools fall short. The role also requires close collaboration across teams: aligning with different stakeholders' needs and priorities, and building strong, ongoing partnerships across the organization.
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) using frameworks like LangGraph / LangChain
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 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.

We are looking for an AI Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
Youll design, build, and ship the AI systems behind Alex: production agents that reason over customer identity data, call real tools, and complete IAM tasks end to end. Starting from a cybersecurity need, youll frame the technical problem, choose an approach, and own the solution through production: architecture, implementation, evaluation, deployment, and iteration on real customer behavior.
Youll build on our companys shared agent infrastructure and extend it: new tools and integrations, workflow orchestration, context and memory management, evaluation harnesses, and the guardrails that keep an autonomous agent safe inside a customers environment.
This is a hands-on engineering role, not a research position. The goal is not to prove that an idea can work; it is to make it useful, reliable, and observable for customers in production.
What You'll Do:
Design, implement, and ship AI-powered features for complex cybersecurity workflows, from prototype to production.
Build the systems that make agents work in practice: tool integrations, planning and orchestration logic, context management, structured outputs, and recovery from failures.
Choose the right technical approach for each problem, combining models, deterministic logic, data, and conventional software where appropriate.
Define quality for each use case and build the evaluation pipelines that measure correctness, task completion, consistency, safety, latency, and cost, and wire them into CI and release.
Debug agent behavior in production: trace failures to their root cause in the prompt, tool, data, or architecture, and fix it.
Own the reliability, observability, and cost of the agent systems you build.
Collaborate with cybersecurity experts, product, and platform engineers throughout development.

run:ai, is looking for a senior software engineer to build the tools that help engineers develop, validate, and contribute software with confidence. as we expand our open-source ecosystem and adopt agentic development, you'll shape how contributions are tested and integrated, improve feedback for developers, and help protect our codebase and internal assets. you'll work across Developer tooling, compatibility validation, scale testing, and ai-assisted contribution workflows, building infrastructure that supports both our engineering teams and the wider open-source community.
what you'll be doing:
build Developer tools and ci workflows that simplify development, testing, and contribution for internal engineers and open-source contributors.
develop compatibility validation infrastructure that verifies run:ai across supported software versions and environments.
build scale-testing and performance-regression infrastructure that produces repeatable results and actionable diagnostics.
develop workflows for agentic prs, including automated evaluation, testing, and controls that support effective human review.
partner with engineering, DevOps, and security teams to enable external contributions while protecting credentials, internal systems, and code integrity.
own projects from technical design through rollout, measuring their impact on Developer productivity and software reliability.

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

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

to join Research team. You'll help build our core intelligence engine: LLM agents that run cloud security workflows end to end. These agents investigate runtime detections, reason over workload, identity, and cloud context, and drive remediation in production. You'll research new models and agent designs, run rigorous experiments, and ship what works as high-impact features in our platform.
What you will do:
Build LLM-based agents that investigate, triage, and remediate cloud security threats.
Own the path from research to production: prototype new models, techniques, and agent designs, and ship the ones that work.
Train, fine-tune, and evaluate models for security tasks using vast data sources: runtime events, cloud logs, identity, and network activity.
Build and extend our internal AI platform: services, pipelines, evals, and observability.
Collaborate with Security Researchers, Product, and Engineering to turn research findings into production-grade autonomous workflows.