AI Engineering - Senior/Lead Engineer (Applied AI)

TL;DR

AI Engineering - Senior/Lead Engineer (Applied AI): Building production AI systems and agentic workflows that integrate LLMs, RAG pipelines, and MCP-based tooling into hirify.global’s analytics and decision management platform with an accent on reliability, evaluation rigor, and responsible AI governance. Focus on designing orchestration/tool-use/memory layers, running offline and online evaluation pipelines, and optimizing inference performance, throughput, and cost under real-world business constraints.

Location: Work from Home, United States

Salary: $140,000 to $220,000

Company

hirify.global is a global analytics software company helping businesses make better decisions using advanced AI and data technologies.

What you will do

• Design and build production AI systems, including agents, RAG pipelines, and LLM-powered workflows integrated into hirify.global’s platform.
• Translate product requirements into technical designs balancing model capability, latency, cost, and reliability.
• Develop evaluation frameworks and benchmarks for quality, safety, and regression; use signals to drive iterative improvement.
• Deliver end-to-end AI features: prompt/context engineering, tool/function design, reusable well-tested code, and offline/online evaluations.
• Build and operate the application layer around foundation models (orchestration, tool use, memory, retrieval, guardrails, observability, human-in-the-loop).
• Optimize inference performance and cost across the serving stack (caching, batching, routing, model selection) and apply AI engineering practices across heterogeneous infrastructure.

Requirements

• Experience shipping complex, production-grade software systems powered by LLMs or other foundation models.
• Hands-on experience deploying LLM-based features in production, including prompt/context engineering, tool/function calling, agentic workflows, and evaluation-driven iteration.
• Strong coding skills in Python and/or TypeScript, with experience using modern AI SDKs/frameworks (e.g., Anthropic/OpenAI/Google SDKs; LangChain, LlamaIndex, LangGraph; agent frameworks; MCP).
• Knowledge of foundation model behavior in practice, including failure modes and experience with fine-tuning, distillation, or model adaptation when needed.
• Familiarity with production LLM architectural patterns: orchestration, tool use, memory, guardrails, observability, caching, and cost/latency optimization.
• Must be based in the United States.

Culture & Benefits

• Inclusive culture aligned with core values: Act Like an Owner, Delight Our Customers, Earn the Respect of Others.
• Highly competitive compensation and benefits/rewards programs.
• People-first work environment with work/life balance, employee resource groups, and social events.
• Opportunities for professional growth through learning experiences and leveraging individual strengths.

Hiring process

• Interviews to assess applied AI engineering experience, production delivery, and evaluation/optimization practices.
• Technical evaluation focused on building and operating LLM-based systems and agentic workflows.
• Discussion of collaboration approach across engineering, product, and data science teams.