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Air

Subscription-based digital asset management for marketers

Senior AI Engineer

AI Application Engineering +1Updated on Nov 11
Full-timeNo salary listed

About the Job

Requirements

  • At least 5 years of experience building production software, AI/ML systems, distributed systems, or similar technical systems.
  • Deep experience designing, building, and operating production AI agents or agent platforms.
  • Experience developing core agent infrastructure, including agent runtimes, tool execution, context management, memory, orchestration, or related platform capabilities.
  • Strong understanding of state-of-the-art agent architectures and the engineering tradeoffs involved in building reliable, production-grade agentic systems.
  • Experience designing agents that use tools, reason across multi-step tasks, interact with external systems, and operate over long-running or complex workflows.
  • Experience building evaluation systems for agents, including task-level evaluations, behavioral evaluations, regression testing, and production quality measurement.
  • Strong understanding of modern large language model systems, including model inference, context engineering, structured outputs, tool calling, retrieval, model selection, and techniques for improving agent performance.
  • Demonstrated ability to evaluate emerging models, research, and agent techniques and translate promising approaches into production systems.
  • Strong programming experience in Python and experience building production-quality software.
  • Experience designing scalable APIs, services, asynchronous systems, and event-driven architectures.
  • Experience operating production services using Kubernetes and cloud platforms such as AWS, GCP, or Azure.
  • Strong understanding of distributed systems, containers, service orchestration, networking, storage, and scalable architectures.
  • Comfort debugging complex failures spanning model behavior, agent execution, application code, and production infrastructure.
  • Ability to move quickly between research and engineering by prototyping ideas, evaluating them rigorously, understanding why they work or fail, and turning successful approaches into reliable production systems.
  • Comfort working at the frontier of a rapidly evolving field where established patterns may not yet exist.
  • U.S. citizenship is required.

Responsibilities

  • Design, build, and improve production agentic AI systems used to solve complex real-world problems.
  • Develop agent architectures for reasoning, planning, tool use, context management, memory, and multi-step task execution.
  • Build tools and capabilities that allow agents to securely interact with data, APIs, code, and external systems.
  • Develop model and inference infrastructure supporting multiple commercial and open-weight language models.
  • Evaluate new models, inference techniques, and emerging AI capabilities and determine how they can improve production systems.
  • Build automated evaluation frameworks to measure agent quality, reliability, task completion, and regressions.
  • Develop datasets, benchmarks, and evaluation methodologies for complex agentic workflows.
  • Improve agent performance through prompt and context engineering, model selection, tool design, inference strategies, and architectural improvements.
  • Build scalable APIs, services, and infrastructure supporting agent execution and AI-powered product experiences.
  • Design systems for asynchronous and long-running agent workflows.
  • Build infrastructure for safe and reliable execution of agent-generated code and other computational workloads.
  • Improve system observability through structured logging, metrics, distributed tracing, dashboards, and automated alerting.
  • Investigate failures across models, agents, application code, and distributed infrastructure and turn findings into systematic improvements.
  • Optimize model and agent systems for latency, throughput, reliability, and infrastructure cost.
  • Translate new AI research and emerging techniques into practical improvements to production systems.
  • Work closely with product, platform, security, and domain teams to bring new AI capabilities from experimentation to production.

Desired Qualifications

  • Current possession of a U.S. security clearance, or ability to obtain one with the employer's sponsorship.
  • Experience in or exposure to the nuances of a startup or other entrepreneurial environment.
  • Experience building secure code execution environments or sandboxes for AI agents.
  • Experience with multi-agent architectures, agent-to-agent communication, or distributed agent execution.
  • Experience with fine-tuning, post-training, reinforcement learning, or synthetic data generation.
  • Experience building AI observability, tracing, and debugging infrastructure.
  • Experience optimizing inference latency, throughput, GPU utilization, or model-serving costs.
  • Experience with AI security, adversarial testing, or securing agentic systems.
  • Experience working in government, defense, or other mission-critical environments.

About the Company

Air

Subscription-based digital asset management for marketers

Air Inc. provides digital asset management software that helps marketers and creatives organize, find, approve, and share digital content. Its core product automates asset organization, search, approval workflows, and sharing within a subscription-based platform, enabling teams to collaborate more efficiently. The company differentiates itself through workflow automation and a community-driven approach, offering a Beta and Research program where users test features and influence development, and by supporting social initiatives (donating 100% of merchandise profits to the Red Hook Initiative). Air Inc.'s goal is to boost productivity for marketing and creative teams while building a collaborative user community and supporting social causes.

Company size
201-500
Company stage
Series B
Total funding
$70.5M
Headquarters
New York City, New York
Founded
2017

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