Manager, Agentic Systems Engineering

About Eightfold Eightfold is a global leader in AI-native enterprise talent platforms, trusted by many of the world’s largest Fortune 500 organizations. Our platform is built from the ground up to operate at scale across Azure and AWS, deployed in multiple regions globally, including IL4-compliant environments for the U.S. Government, supporting users in 100+ countries and 30+ languages. Eightfold is at the forefront of agentic AI, delivering intelligent agents that actively drive outcomes across hiring and talent workflows, while much of the industry is still in the experimental phase. Backed by $410M+ in funding and valued at $2B+, we are defining the next era of agentic talent systems. What sets Eightfold apart is not just our technology and mission, but our people. We are a deeply technical, execution-driven organization that values ownership, collaboration, and high standards. Our teams work closely across functions and locations to build systems that scale in real-world enterprise environments. The Mission We are building production-grade AI agents that automate high-value workflows across the organization — from investigating and resolving customer support issues, to automating quality assurance, to generating product documentation, to powering cross-functional workflow automation for Sales, CX, Legal, and Marketing teams. These aren’t chatbots or GPT wrappers. They are agentic systems that reason over real data, call tools across enterprise platforms (Salesforce, Slack, Confluence, internal APIs), and take meaningful actions in production. You will lead a team of engineers responsible for designing, building, and shipping this portfolio of agents. This is a hands-on leadership role — you will be in the code alongside your team, architecting systems, reviewing PRs, debugging production issues, and personally shipping features when the situation demands it. What You’ll Do Own and execute: Drive the roadmap, execution, and delivery of multiple agentic systems across support automation, QA automation, documentation generation, and cross-org workflow automation. You are accountable for shipping production-quality agents on schedule. Architect agentic systems: Design multi-agent architectures involving LLM orchestration, tool-calling, retrieval-augmented generation (RAG), structured output parsing, and human-in-the-loop approval workflows. Stay hands-on: Write and review code daily. Debug agent failures, optimize prompts and pipelines, and troubleshoot integration issues alongside your engineers. Drive reliability and quality: Move beyond "it works in a demo." Own the iterative refinement of agent accuracy, hallucination detection, output validation, and safety guardrails. Define quality measurement frameworks, build observability and monitoring into every agent, and establish feedback loops that drive continuous improvement. Build for scale and security: Design agents that operate reliably at enterprise scale. Think critically about rate limiting, cost controls, data privacy, prompt injection prevention, and secure handling of sensitive information across integrations. Lead and grow the team: Manage a team of engineers in a fast-paced, agile environment. Set technical direction, remove blockers, and mentor engineers in agentic system design and production best practices. Measure what matters: Define and track KPIs for each agent — resolution accuracy, coverage, time saved, adoption — and use data to prioritize what to build next. What You Bring Strong engineering fundamentals: Deep hands-on experience with Python. You can architect a system, write production code, debug a concurrency issue, and review a PR with equal confidence. Agentic AI experience: You’ve built or led teams building agentic systems. You understand tool-calling, multi-step reasoning, context window management, hallucination mitigation, and prompt engineering for reliability. Production mindset: You know what it takes to run AI systems in production — retry logic, error handling, monitoring, alerting, graceful degradation, and quality measurement to track agent performance over time. Scale and security thinking: You think about how agents behave at scale — rate limits, cost management, data privacy, and secure access patterns. You design systems that are safe by default and resilient under load. System design chops: Experience designing distributed systems with message queues, producer-consumer patterns, cloud storage, and API integrations. Engineering leadership: Proven experience managing engineering teams. You set high technical standards, give constructive code reviews, and create an environment where engineers ship with confidence. AI-native workflow: You actively leverage AI coding tools and assistants to maximize your productivity and your team's output. You stay current with the rapidly evolving AI tooling landscape and bring best practices to the team. Nice to Have Experience with workflow orchestration tools (n8n, LangChain, LangGraph, CrewAI, or similar). Familiarity with RAG architectures, vector databases, and semantic search. Background in support engineering or QA — understanding the pain points these agents solve.