AI Engineer
Autofleet seeks an AI Engineer to design and deploy enterprise AI agents that automate high‑value workflows across mobility operations. The role focuses on building an end‑to‑end agent stack, including tool integration, memory management, multi‑step planning, and human‑in‑the‑loop escalation.
Skills required
About this role
We are making the future of Mobility come to life starting today.
At Autofleet we support the world's largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.
We are looking for a talented AI Engineer to help us develop advanced AI solutions that will empower impactful projects. The Data Science team leads the AI research and innovation efforts of the company, and is focused on pushing AI boundaries to enhance the product and optimize processes.
What You’ll Do
- Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions
- Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails and audit trails.
- Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
- Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
- Collaborate with engineering teams to design, build, and maintain production pipelines.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline.
- 5+ years of software engineering experience, with proven ability to build production-grade systems beyond research, experimentation, or prompt engineering.
- Hands-on experience building and operating production-grade AI/LLM systems, ideally including agentic workflows, tool-calling, orchestration, evaluations, or multi-step reasoning systems.
- Strong backend expertise, including Python, distributed systems, and cloud-native architecture.
- Familiarity with the AI Agents ecosystem, including different LLM providers, vector store solutions, MCPs, A2A and more.
- Familiarity with REST APIs and integrating external data sources.
