דרושים » הנדסה » AI Agent Engineer

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.
Responsibilities:
AI Agent Design & Integration:
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development:
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure:
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement:
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.

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 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 are looking for a Senior AI Engineer to design and build the intelligence layer .

You will own the agentic workflows, LLM integrations, and reasoning 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.

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 the 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 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 evolves from AI-enhanced internal tools to customer-facing AI products.

We Are more than a Professional Services firm, we are a community of people driven by impact. As part of a leading global network, we partner with the most influential organizations to drive large-scale business and technology transformation, combining strategic thinking, innovation, and advanced technologies. Guided by integrity, excellence, and forward thinking, we help organizations navigate complexity and create real change. Our people are at the center, with space to lead, experiment, and grow in a dynamic and collaborative environment that connects personal development with meaningful impact on the future of business and society. About the Role We're looking for a AI backend Engineer with a data science foundation to join our AI team and build production systems that integrate Generative AI capabilities. You'll design and implement end-to-end solutions APIs, databases, orchestration, and cloud infrastructure for clients across a wide range of sectors. This role is ideal for someone who thinks in systems, enjoys working with GenAI technologies, and wants to grow into designing complex architectures. You'll work alongside senior engineers on real delivery projects from day one. Key Responsibilities Design and build backend systems APIs, databases, authentication, and integrations Implement multi-agent architectures and orchestration workflows combining LLMs with backend logic Deploy and maintain systems on GCP, Azure, or AWS Optimize AI system performance, cost, and reliability Collaborate with client teams, DevOps, and stakeholders on project delivery Contribute to technical discussions and solution design

As an AI Product Manager on the Paradox team, you will own the product lifecycle for core LLM-powered capabilities across candidate and recruiter experiences.
Youll drive product discovery, define requirements, and partner with engineering and data science from design through launch, iteration, and ongoing optimization.
One key part of this role is hands-on AI evaluation. You will regularly review LLM outputs and AI conversation logs, perform data labeling, and dig into traces to identify gaps, edge cases, and failure modes. Youll translate these insights into concrete changes to prompts, data, workflows, and system logic that measurably improve quality, reliability, and user experience.
You will:
Own AI-driven features, including candidate and recruiter-facing experiences.
Define product vision, goals, and roadmaps for your areas, balancing user value, technical feasibility, and business impact.
Lead rigorous evaluation of LLM behavior: review outputs, label data, and partner with data science to refine prompts, metrics, and evaluation strategies.
Collaborate closely with engineering to design scalable, robust solutions and iterate quickly on experiments.
Synthesize input from customers, internal stakeholders, and the market into clear, prioritized product requirements.
Communicate progress, tradeoffs, and learnings across a global, cross-functional set of partners, guiding teams toward high-impact outcomes.
This role is ideal for someone excited to be in the weeds with AI systems-understanding how they behave, why they fail, and what to adjust to make them meaningfully better for real users.