Lead Engineer, AI Platform

Lever, Inc.; Ireland; Remote; EUR 136,000 - 166,000; Full time

Benefits offered by this job

Annual cash compensation: $170,000 USD

Equity with refresh grants

35 days paid time off per year

Fully remote work environment

Global company retreats

Job summary

Lever, Inc. seeks a Lead Engineer, AI Platform in Ireland to build evaluation frameworks, observability tooling, and diagnostics for production AI agents. You will lead hands-on engineering and people management, shaping the AI Quality function.

In a highly remote, asynchronous environment, you will guide experiments, datasets, and model comparisons, while optimizing cost, latency, and user experience across complex AI workflows.

Qualifications

• 7+ years of experience building and shipping production software.
• Experience with complex, tool-using AI systems involving multiple tools, planning, orchestration, or sub-agents.
• Strong ability to demonstrate shipped software and explain how its effectiveness and reliability were measured.
• Experience with Ruby on Rails and/or Python, with the ability to become productive quickly in technologies that may be new to you.
• Experience building evaluation or observability infrastructure for ML/AI systems, including evaluation pipelines, scorers, dashboards, or CI/CD systems for evaluations.
• Familiarity with evaluation frameworks such as Braintrust, LangSmith, or similar tools.
• Experience designing datasets, annotation workflows, or labeling pipelines for machine learning or AI evaluation.
• Ability to learn quickly, experiment extensively, and use empirical results to guide technical decisions.
• Comfortable operating in a fast-paced environment with ambiguity and changing technical requirements.
• Strong technical leadership and people-management capabilities, with the ability to balance team leadership and hands-on engineering.
• Excellent English proficiency in spoken, written, and reading communication, equivalent to CEFR C2 / ILR 5.

Responsibilities

• Design, build, and own evaluation infrastructure, including CI/CD pipelines, scorers, datasets, and systems for assessing AI agents from individual tool calls through complete multi-turn conversations.
• Develop observability and diagnostic capabilities to identify exactly where quality issues occur across planning, execution, tool selection, and complex agent trajectories.
• Investigate failures across sophisticated AI workflows and turn findings into technical prototypes, improvements, or clearly defined priorities for AI engineering teams.
• Build and expand datasets through human annotation, AI-generated examples, and simulated conversations to increase evaluation coverage efficiently.
• Develop structured experimentation frameworks for prompts, models, and agent harnesses, including evaluation of new and open-source models against production baselines.
• Identify opportunities to improve AI system cost and latency through model selection, caching, routing, and other optimization strategies.
• Set the technical direction and manage day-to-day priorities for the AI Quality engineering team while remaining actively involved in hands-on development.
• Partner closely with AI Core engineering teams to ensure changes to AI products can be measured effectively and demonstrably improve quality.
• Establish engineering practices and evaluation approaches that support reliable, efficient, and scalable production AI systems.

Skills

Technical leadership

People management

English proficiency

Hands-on engineering

Fast-paced environment

Empirical decision making

Tools

Ruby on Rails

Python

Braintrust

LangSmith

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Engineer, AI Platform based in Ireland.

This role offers the opportunity to lead the engineering foundation behind reliable, measurable, and scalable AI-powered features. You’ll build evaluation frameworks, observability tooling, and diagnostic infrastructure that reveal how AI agents perform in real production environments. The position combines hands‑on software engineering with technical leadership and people management. You’ll investigate quality issues across complex agent workflows, develop datasets and evaluation systems, and run experiments across models, prompts, and agent architectures. You’ll also help optimize AI systems for cost, latency, reliability, and overall user experience. Working in a highly remote and asynchronous environment, you’ll collaborate closely with AI engineering teams while shaping the technical direction of a growing AI Quality function.

Accountabilities

• Design, build, and own evaluation infrastructure, including CI/CD pipelines, scorers, datasets, and systems for assessing AI agents from individual tool calls through complete multi‑turn conversations.
• Develop observability and diagnostic capabilities to identify exactly where quality issues occur across planning, execution, tool selection, and complex agent trajectories.
• Investigate failures across sophisticated AI workflows and turn findings into technical prototypes, improvements, or clearly defined priorities for AI engineering teams.
• Build and expand datasets through human annotation, AI‑generated examples, and simulated conversations to increase evaluation coverage efficiently.
• Develop structured experimentation frameworks for prompts, models, and agent harnesses, including evaluation of new and open‑source models against production baselines.
• Identify opportunities to improve AI system cost and latency through model selection, caching, routing, and other optimization strategies.
• Set the technical direction and manage day‑to‑day priorities for the AI Quality engineering team while remaining actively involved in hands‑on development.
• Partner closely with AI Core engineering teams to ensure changes to AI products can be measured effectively and demonstrably improve quality.
• Establish engineering practices and evaluation approaches that support reliable, efficient, and scalable production AI systems.

Requirements

• 7+ years of experience building and shipping production software, ideally including LLM‑powered agents capable of taking real actions within products.
• Experience working with complex, tool‑using AI systems involving multiple tools, planning, orchestration, or sub‑agents rather than only simple, single‑turn assistants.
• Strong ability to demonstrate shipped software and explain how its effectiveness and reliability were measured.
• Experience with Ruby on Rails and/or Python, with the ability to become productive quickly in technologies that may be new to you.
• Experience building evaluation or observability infrastructure for ML/AI systems, including evaluation pipelines, scorers, dashboards, or CI/CD systems for evaluations.
• Familiarity with evaluation frameworks such as Braintrust, LangSmith, or similar tools.
• Experience designing datasets, annotation workflows, or labeling pipelines for machine learning or AI evaluation.
• Ability to learn quickly, experiment extensively, and use empirical results to guide technical decisions.
• Comfortable operating in a fast‑paced environment with ambiguity and changing technical requirements.
• Strong technical leadership and people‑management capabilities, with the ability to balance team leadership and hands‑on engineering.
• Excellent English proficiency in spoken, written, and reading communication, equivalent to CEFR C2 / ILR 5.
• Strong alignment with a collaborative, ownership‑oriented engineering culture.

Benefits

• Annual cash compensation of $170,000 USD, benchmarked to U.S. compensation levels regardless of location.
• Equity in the company, including ongoing refresh grants.
• 35 days of paid time off per year.
• Fully remote work environment.
• Significant flexibility and autonomy in how you organize your work.
• Twice‑yearly company retreats in international destinations.
• Benefits supporting health, wellbeing, and professional development.
• Opportunity to lead and grow an AI Quality engineering team while remaining hands‑on technically.
• Exposure to advanced AI agents, evaluation infrastructure, observability, experimentation, and production AI optimization.
• Opportunity to work with a globally distributed team across multiple countries and time zones.

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