Summary
What you’ll impact
The Senior Frontier Agent Engineer (Applied AI) at the organization will design, evaluate, and deploy production AI agents that integrate large language models with structured knowledge, retrieval, and traditional machine learning for enterprise customers. The role spans the full AI system lifecycle—from research translation and experimentation to building reliable, observable production systems and collaborating directly with customers across multiple industries.
Responsibilities
What you'll do
- Bridge the gap between cutting-edge AI research and production deployment.
- Work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software.
- Build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company.
- Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use.
- Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data, and deterministic software into reliable production workflows.
- Engineer customer intelligence layers, retrieval pipelines, memory systems, and knowledge representations that allow agents to reason over large, heterogeneous enterprise data.
- Develop multi-agent systems that coordinate reasoning, planning, tool execution, and human oversight.
- Translate frontier AI research into production systems by rapidly evaluating new models, prompting techniques, reasoning paradigms, and agent architectures.
- Own the full experimentation lifecycle, from hypothesis generation to production rollout.
- Design rigorous evaluation frameworks using offline benchmarks, online A/B experiments, golden datasets, regression suites, LLM-as-a-Judge, and human evaluation.
- Run controlled experiments and ablation studies to understand the contribution of different models, prompts, retrieval strategies, reasoning techniques, memory systems, and agent architectures.
- Continuously evaluate newly released frontier models and determine where they meaningfully improve quality, latency, reliability, or cost.
- Develop confidence estimation, reflection, and continuous learning systems that improve agents over time using real-world feedback.
- Measure success through business outcomes, not benchmark scores.
- Build production-quality AI systems with a strong emphasis on reliability, observability, latency, safety, and cost.
- Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines that enable safe deployment in high-stakes environments.
- Collaborate with infrastructure engineers to deploy AI systems securely within enterprise cloud environments.
- Build human-in-the-loop workflows that effectively combine AI automation with expert oversight.
- Partner directly with enterprise customers to understand their business, data, and operational challenges.
- Translate ambiguous customer problems into production AI architectures.
- Rapidly prototype new ideas, validate them with customers, and evolve successful solutions into scalable production systems.
- Identify reusable patterns that become core capabilities across many enterprise deployments.
- You'll work across the full lifecycle of modern AI systems:
- Designing reasoning and agent architectures
- Building retrieval, memory, and customer intelligence systems
- Developing predictive models that work alongside LLMs
- Running experiments and ablation studies
- Shipping production systems into enterprise environments
- Measuring business impact through online experimentation
- Continuously improving agents using real-world feedback
Requirements
What you’ll bring
- 5+ years of software engineering, machine learning, or applied AI experience.
- Strong Python programming skills.
- Experience building production AI systems using LLMs.
- Experience with modern AI tooling, including OpenAI, Claude, MCP, agent frameworks, vector databases, or retrieval systems.
- Strong understanding of machine learning fundamentals and modern language models.
- Experience designing or evaluating AI systems using quantitative metrics.
- Excellent communication skills and the ability to work directly with enterprise customers.