דרושים » AI » Senior AI Engineer

We are looking for a highly motivated AI Full stack Engineer with GenAI background in production to join our team and help us shape the future of the Agentic engineering platform (AEP).
What youll do:
At our company, were a platform by developers, for developers. Your role will encompass end-to-end design, implementation, and daily feature delivery across both backend and frontend systems.
You will:
Implement high scale AI-powered features deeply integrated into our platform
Design and build production-grade backend systems serving a wide and growing user base
Build agent-based workflows using frameworks such as AI SDK
Integrate LLMs into real production systems with attention to reliability, latency, observability, and cost
Work across frontend (React + TypeScript) and backend (NodeJS, Python, Go) to deliver complete AI-driven user experiences
Own features end-to-end: design, implementation, testing, deployment, and monitoring
Help define standards and best practices around AI reliability and evaluation
Contribute to technical planning, mentor teammates, and help recruit top talent
Develop retrieval-augmented generation (RAG) pipelines over structured and unstructured data
Our stack includes React + TypeScript on the frontend, and NodeJS + TypeScript, Python, and Golang on the backend, and Vercels AI-SDK + AWS Bedrock + Azure OpenAI for GenAI. We use Kafka + Kafka Connect, Redis, PostgreSQL, MongoDB and other modern infrastructure components.

We are seeking an experienced, hands-on Senior AI Engineer to join the Generative AI applications Platform group and lead the backend implementation and architecture of AI/LLM solutions - from agent graphs and tooling to RAG, streaming, and production deployment.
As a Senior ML Engineer you will
Design and own agent architectures - Build and evolve graph-based agent workflows (multi-node LLM flows, tool execution, routing, human-in-the-loop review gates) using LangGraph, with clear state schemas, checkpointing, and streaming to production.
Turn product and user needs into backend AI - Work with Engineers, Product, and Analysts to translate business problems into technical requirements and implementations, including agent types, tools, RAG pipelines, and configuration-driven behavior.
Design, develop, and deploy GenAI capabilities end-to-end - LangChain tools and integrations, RAG (retrievers, vector stores, agentic flows), structured outputs, and APIs for chat, Copilot-style integrations, and MCP.
Raise the bar on quality and reliability - Establish patterns for observability (e.g., LangSmith), error handling, content safety, bounded autonomy (tool schemas, review workflows), and evaluation systems so that AI behavior is predictable and auditable.
Mentor and align the team - Provide technical guidance on LLM backend architecture and LangGraph/LangChain best practices so the team can iterate quickly and safely.

Required Software Engineer - Al Platform
What this role is really about:
You're building our AI platform. The internal system that powers AI capabilities across Product, Customer Success, Sales, Operations, Data, and IT.
This is a full-stack role where you'll own features end-to-end: design React interfaces for AI workflows, build Lambda functions that orchestrate multi-agent processes, integrate with enterprise systems (Salesforce, Workato, Snowflake), and optimize costs and performance at scale. You'll work with cutting-edge AI while building production-grade systems that handle real business operations.
If you want to build something that directly enables business growth, work across the full stack with modern tech, and have ownership over a platform that the entire company depends on, this is your opportunity.
Job responsibilities:
Build core AI platform services - Design and implement agent orchestration, prompt management, RAG, Connectors, and evaluation pipelines that power AI experiences across the company.
Develop complex agentic process - Develop a multi-step workflow that coordinates tools and services with proper observability, guardrails, and cost controls (using OpenAI Agent SDK, LangGraph, or a similar framework).
Build LLM evaluation and optimization process -Develop evaluation harnesses, offline/online experiments, prompt-testing frameworks, and dashboards to balance quality, latency, and spend across all AI services.

Required Senior Software Engineer, AI Platform
About the role
As a Senior Software Engineer - AI, youll design, build, and own production grade AI agents that operate at the core of our cloud security platform. Youll work on distributed, cloud native services that embed agentic AI workflows into our existing microservices architecture.
This role goes beyond building AI logic: youll be responsible for operating AI systems in production, ensuring they are observable, reliable, and continuously improving through systematic evaluation and data driven iteration.
On a typical day youll:
Design and implement cloud-native, distributed services that power our AI-driven security features
Build and maintain agentic AI systems that reason over large-scale cloud security data and interact with multiple internal services
Own AI agents in production, including deployment, monitoring, troubleshooting, and performance optimization
Implement observability for AI systems, including metrics, logging, tracing, and alerting for agent behavior, quality, latency, and cost
Develop continuous evaluation pipelines for agentic solutions, including offline testing, regression detection, and production feedback loops
Design and optimize RAG pipelines that operate reliably over high-volume, high-variance security data
Apply strong software engineering practices: clear APIs, clean abstractions, robust error handling, and scalable data flows
Lead services end to end - from design and implementation to deployment and long-term operation
Collaborate closely with Data Platform, Product, and Security Research teams to ensure AI behavior is correct, explainable, and trustworthy.

We are seeking a Backend Team Lead to spearhead the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact leadership role at the intersection of backend architecture, multimodal AI, and real-time systems. You will architect and lead the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Lead & Mentor: Build and manage a high-performing backend/AI engineering team, drive architectural decisions, and foster rapid innovation while maintaining production-grade reliability.
Design AI-Native Systems: Architect scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Design distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

It starts with you - an AI Engineering Team Lead responsible for leading a team building production-grade agentic systems. This role combines hands-on technical leadership with people management, ownership of architecture, delivery, and quality across agents, MCP tools, agentic workflows, RAG systems, and evaluation.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Lead and mentor a team of AI Engineers building agentic systems and MCP tools.
Own technical direction for agent architectures, workflows, and LLM evals.
Review designs and code; ensure production readiness, reliability, and security.
Drive adoption of frameworks such as LangChain, LangGraph, or equivalent.
Set standards for RAG pipelines, vector databases, and retrieval quality.
Partner with product and platform teams on roadmap, scope, and delivery.
Balance hands-on contribution with people management and execution.
Build a culture of strong engineering practices, measurement, and iteration.

Required AI Developer
Who we are:
We are a TLV-based startup thats on a mission to change the work experience for 80% of the worlds global workforce - the deskless employees.
Our business management platform helps thousands of businesses thrive by taking away the daily hustle and complexities of managing teams, so they can have the peace of mind to grow and run their business.
About the Role:
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 seeking a Backend Engineer to join the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact role at the intersection of backend architecture, multimodal AI, and real-time systems. You will contribute to the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Design AI-Native Systems: Design and implement scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design and implement multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Contribute to distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

It starts with you - a senior AI engineer focused on building production-grade agentic systems: autonomous agents, MCP tools, agentic workflows, and rigorous evaluation.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Design and build single- and multi-agent systems with planning, memory, and tool use.
Implement and operate MCP tools and servers with secure schemas and permissions.
Build agentic workflows using LangChain, LangGraph, or equivalent frameworks.
Integrate LLMs via SDKs; manage prompts, structured outputs, and tool calling.
Define and run LLM evals (quality, correctness, latency, cost, regressions).
Add observability and reliability (logging, tracing, retries, state management).
Optimize performance and cost; take systems to production.
Mentor engineers and establish agentic best practices.

Were looking for a Generative AI Developer to join our forward-thinking engineering team. This role is perfect for someone with a passion for cutting-edge AI, a strong software engineering background, and the creative spark to identify and implement novel use cases within our product.

You will play a critical role in adding AI capabilities to our FinOps SaaS platform. Whether it's enhancing user workflows, automating insights, or inventing entirely new product experiences, youll have both the freedom and support to experiment and execute.

We provide a uniquely rich dataset covering the full scope of a companys cloud spend. This expansive data playground offers a powerful foundation for experimentation and insight generation, enabling the development of intelligent, value-driven features.

Responsibilities:
Lead the charge in transforming our product and preparing it for the agentic age.
Design, build, and deploy generative AI-powered features across our product.

Identify opportunities for AI integration by proactively exploring FinOps use cases and user needs.

Prototype and validate new AI use cases quickly and iterate based on internal and external feedback.

Collaborate cross-functionally with product, design, and backend teams to drive innovation from concept to production.

Stay current with the fast-moving generative AI landscape and evaluate new models, APIs, and tools (e.g., OpenAI, Anthropic, Hugging Face, AWS Bedrock, open-source LLMs).

Live in the future and track new innovations and paradigms in this fast evolving field and identify opportunities to integrate them into the product.

Implement safeguards, prompt engineering techniques, and usage monitoring to ensure high-quality AI outputs.

Optimize model performance, inference time, and cost efficiency within AWS infrastructure.