Description review
AI Engineer - Agentic Systems
Toptal · USA Only · back to the listing
HR standards
71/100
solid
Title ↔ description
77/100
solid
Reads as
Machine Learning Engineer
80% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Headquarters:
Summary
We are seeking a highly hands-on AI Engineer with a proven track record of shipping agentic AI systems into production for enterprise environments. In this role, you will design, build, and deploy production-grade AI agents capable of multi-agent orchestration, complex tool execution, and dynamic context management. You will implement robust safety guardrails and integrate frontier models using leading agent frameworks and cloud infrastructure. This position is remote within the US (East Coast time zone slightly preferred) with minimal travel required.
Tasks and Deliverables
Design and deploy autonomous and multi-agent orchestration workflows capable of executing complex, multi-step enterprise processes.
Implement structured tool-use frameworks and integrate external APIs and services using protocols such as Model Context Protocol (MCP).
Engineer advanced prompt strategies, dynamic context management, and memory layers to optimize model performance and reasoning accuracy.
Implement production-grade guardrails, schema enforcement, latency optimization, and defensive exception handling across all agentic workflows.
Deploy, scale, and maintain agentic microservices on cloud infrastructure using modern containerized and serverless platforms.
Required Experience
Production Agent Experience: Demonstrated, hands-on software engineering experience building, testing, and shipping autonomous agent systems and tool-calling workflows into production environments.
Framework & Model SDK Expertise: Strong proficiency in at least one major model ecosystem: Anthropic (Claude Agent SDK, Model Context Protocol), OpenAI (OpenAI Agents SDK, Responses API, File Search), or GCP Vertex AI (Gemini, Google ADK / Vertex AI Agent Engine).
Cloud Infrastructure (GCP): Experience building and deploying distributed applications on Google Cloud Platform, using services such as GKE, Cloud Run, Pub/Sub, and BigQuery.
Core Software Engineering: Strong foundation in software design patterns, API integrations, structured outputs (Pydantic/JSON schemas), and microservices architecture.
Location: Based in the US (East Coast time zone slightly preferred).
Engagement Highlights
Enterprise Production Scale: Move beyond proof-of-concept builds to deploy actual, living agent systems that automate mission-critical enterprise processes.
Frontier Tech Stack: Work directly with state-of-the-art agent SDKs, Model Context Protocol, and multi-model ecosystems across Anthropic, OpenAI, and GCP.
Remote Autonomy: Enjoy a fully remote US-based role with high execution agency and low friction.
To apply: https://weworkremotely.com/remote-jobs/toptal-ai-engineer-agentic-systems
Summary
We are seeking a highly hands-on AI Engineer with a proven track record of shipping agentic AI systems into production for enterprise environments. In this role, you will design, build, and deploy production-grade AI agents capable of multi-agent orchestration, complex tool execution, and dynamic context management. You will implement robust safety guardrails and integrate frontier models using leading agent frameworks and cloud infrastructure. This position is remote within the US (East Coast time zone slightly preferred) with minimal travel required.
Tasks and Deliverables
Design and deploy autonomous and multi-agent orchestration workflows capable of executing complex, multi-step enterprise processes.
Implement structured tool-use frameworks and integrate external APIs and services using protocols such as Model Context Protocol (MCP).
Engineer advanced prompt strategies, dynamic context management, and memory layers to optimize model performance and reasoning accuracy.
Implement production-grade guardrails, schema enforcement, latency optimization, and defensive exception handling across all agentic workflows.
Deploy, scale, and maintain agentic microservices on cloud infrastructure using modern containerized and serverless platforms.
Required Experience
Production Agent Experience: Demonstrated, hands-on software engineering experience building, testing, and shipping autonomous agent systems and tool-calling workflows into production environments.
Framework & Model SDK Expertise: Strong proficiency in at least one major model ecosystem: Anthropic (Claude Agent SDK, Model Context Protocol), OpenAI (OpenAI Agents SDK, Responses API, File Search), or GCP Vertex AI (Gemini, Google ADK / Vertex AI Agent Engine).
Cloud Infrastructure (GCP): Experience building and deploying distributed applications on Google Cloud Platform, using services such as GKE, Cloud Run, Pub/Sub, and BigQuery.
Core Software Engineering: Strong foundation in software design patterns, API integrations, structured outputs (Pydantic/JSON schemas), and microservices architecture.
Location: Based in the US (East Coast time zone slightly preferred).
Engagement Highlights
Enterprise Production Scale: Move beyond proof-of-concept builds to deploy actual, living agent systems that automate mission-critical enterprise processes.
Frontier Tech Stack: Work directly with state-of-the-art agent SDKs, Model Context Protocol, and multi-model ecosystems across Anthropic, OpenAI, and GCP.
Remote Autonomy: Enjoy a fully remote US-based role with high execution agency and low friction.
To apply: https://weworkremotely.com/remote-jobs/toptal-ai-engineer-agentic-systems