Connecteam

SaaS

GenAIEngineer

Tel Aviv, Israel Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Gen AI Engineer at Connecteam. Skills: AI Agent Design & Integration, Fullstack Product Development, Systems & Infrastructure. Design and implement autonomous or semi-autonomous agents using LLMs. Work with prompt engineering, RAG pipelines, and tool/plugin integrations”

What You'll Achieve.

deliver AI-first features that feel magical to users; measurable performance; drive product and model improvements

Industry & Context.

SaaS

What They're Looking For.

Must Have

8+ years of fullstack development experience, skills in TypeScript/JavaScript, React, and Python (or Node/Go for backend), Product-oriented mindset

Nice to Have

Solid understanding of LLM APIs, agent frameworks (e.g., LangChain, AutoGPT, CrewAI), or custom AI pipelines, Experience with modern cloud infrastructure (e.g., AWS, GCP, Docker, CI/CD), Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG), experience with observability, feedback loops for AI agents, or embedded AI evaluation techniques

What You'll Do.

Design and implement autonomous or semi-autonomous agents using LLMs

Work with prompt engineering

and tool/plugin integrations

Build scalable agent runtimes and orchestration layers

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

Build and maintain backend services and pipelines that support AI agents

Optimize inference flows for performance and cost

Ensure systems are secure

and compliant with InfoSec standards

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

How You'll Work.

Team & Collaboration

Collaborate closely with design, product, and data teams to bring ideas from concept to production; Work closely with a world-class team of engineers, product leaders, and data scientists

Full Job Description

Who Connecteam is: Connecteam is a TLV-based startup that’s on a mission to change the work experience for 80% of the world’s 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: We’re 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

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