AI Agent Jobs
Explore AI agent jobs for engineers and builders designing tool-using agents, LLM applications, multi-agent workflows, evaluations and production agent systems.
Latest agentic AI jobs
201of 201 roles
AI Solutions Engineer
Experience shipping AI agents, workflows, or automations; Python, REST APIs, cloud serverless functions, LLM APIs, SQL, authentication, Git, and workflow automation platforms required.
Full Stack AI Engineer
5+ years FullStack experience; expert TypeScript & React and NodeJS; Python required; PostgreSQL, GraphQL/REST; AI engineering with Claude Code; architecture mindset; mentoring; German C1 or native.
AI Business Engineer
Requires end-to-end project delivery, rapid business-domain learning, AI-native workflows, prompt and context engineering, agent design and evaluation, production Python, LLM API integration, version control, and testing.
AI & Agentic Engineer
3–5 years in software or data engineering; strong Python and TypeScript/JavaScript skills; AI/LLM, RAG, agentic framework, API, React, cloud, and data pipeline experience; English proficiency; bachelor's or master's degree or equivalent.
AI & Agentic Engineer
Requires 3–5 years in software or data engineering, strong Python and TypeScript/JavaScript, React, APIs, RAG, embeddings, vector search, agentic frameworks, an AI platform, cloud experience, English, and a relevant degree or equivalent.
Associate AI Builder
0-2 years experience; AI-native fluency using CLI tools (e.g., Claude Code), ability to architect and code agentic solutions, customer-facing onboarding and program management, eligible for U.S. work authorization and government background check.
AI Architect
Requires 10+ years building production software, 3+ years at architect or staff-engineer scope, agentic tooling, LLM systems, cloud architecture, APIs, observability, containers, infrastructure-as-code, CI/CD, and strong written communication.
AI Platform Engineering Team Lead
Requires 7+ years of software engineering experience, 3+ years leading engineers, production LLM or GenAI experience, distributed systems expertise, and knowledge of agentic systems, RAG, evaluations, and observability.
AI Engineer
Production Python expertise, experience building and debugging agentic systems, familiarity with agent frameworks/protocols, evaluation rigour, and strong engineering discipline (version control, tests, reproducibility).
AI Engineer
7+ years software engineering experience with 3+ years in AI/ML or data engineering; production LLM/agent experience; data pipeline and retrieval expertise; proficiency with modern Python stack and containerized AWS infrastructure.
Lead AI Engineer
3–5 years shipping production software; strong in Python; shipped at least one LLM-powered feature; fluent English; experienced with agentic coding tools (eg Claude Code, Cursor); side projects; daily user of AI tooling.
AI Engineer
5+ years software development and 2+ years building production LLM applications; strong Python, agentic architectures, RAG, embeddings, vector search, APIs, AWS, and collaborative development experience; bachelor's degree or equivalent.
Choosing an AI agent role
Agent roles can appear under titles such as AI engineer, agent engineer, LLM application developer or applied AI builder. Read the description for the actual system: what task the agent performs, which tools it can call, what context it retrieves and how its output reaches a user. Building an agent involves more than writing a prompt; the surrounding application determines whether its behaviour is useful.
For a portfolio, choose a bounded task and show how you evaluate it. Include representative inputs, failure cases, tool permissions and the point at which a person takes over. Explain how you track latency, model costs and unsuccessful runs. Multi-agent designs need a clear reason for dividing the work, alongside a way to inspect the handoffs between agents.
AI automation roles often focus on a repeatable process across business systems. Agent roles put more emphasis on model-driven decisions, tool use and evaluation within that process. Both can involve APIs, retrieval and orchestration, with different amounts of custom code. Check whether a listing expects application engineering, visual workflow building or both, and whether the team needs a prototype or an owner for an existing production system.