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 shifting from "automated testing" to "autonomous quality." You will lead a specialized squad of engineers dedicated to building an ecosystem of AI agents that handle the heavy lifting of the QA lifecycle—from drafting complex test plans and executing automated suites to performing deep-dive Root Cause Analysis (RCA) on customer-reported issues.
This isn't about managing people who click buttons; it's about leading a team that builds the robots that ensure our software is flawless.
What You’ll Do
- Architect the Agent Stack: Lead the design and deployment of AI agents and Python for custom logic, tool-calling, and LLM integration.
- Drive Iterative Quality: Move beyond "it works" to "it’s accurate." You will oversee the iterative refinement of agent prompts, RAG (Retrieval-Augmented Generation) pipelines, and output validation to ensure high-fidelity outcomes.
- Metrics-Driven Leadership: Define and track KPIs that actually matter: Agent Accuracy Rate, RCA Lead Time Reduction, and Test Coverage Expansion via AI.
- Bridge the Gap: Translate high-level QA strategies into technical requirements for AI agents that can analyze customer logs, identify patterns, and suggest test improvements autonomously.
- Hands-on Mentorship: Stay in the code. We need a leader who can troubleshoot a Python script or an agent workflow alongside their team when the going gets tough.
What You Bring
- Technical Depth: Extensive experience with Python and at least one workflow orchestration tool (ideally n8n, or alternatively LangChain/Flowise).
- AI Practicality: You’ve moved past the "GPT wrapper" stage. You understand how to handle hallucinations, manage context windows, and optimize agentic workflows for reliability.
- QA/QE Roots: You understand the fundamentals of test planning, regression testing, and the software development lifecycle (SDLC).
- The "Iterative" Mindset: You are obsessed with the feedback loop.
- Leadership: Experience managing 5+ engineers in an agile, fast-paced environment.
Education & Background
- Academic Foundation: Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a related quantitative field.
- Proven Track Record : 5+ years in Software Engineering or Quality Engineering, with at least 2-3 years of hands-on experience building and deploying AI-enabled applications or automation frameworks.
- Leadership Experience: 2+ years in a formal management or lead role, specifically driving high-velocity engineering teams through the full development lifecycle.
- Soft Skills & Leadership Style
- The "Scientist" Mindset: A passion for experimentation. You don't get frustrated when an agent fails; you analyze the trace, adjust the prompt/data, and try again.
- Strategic Vision: The ability to explain to stakeholders how "AI for QA" translates to faster ship times and lower operational costs.
- Candor & Mentorship: A leader who provides direct, actionable feedback and fosters a culture of "building in public" and sharing AI learnings across the org.
Hybrid Work
Eightfold follows a hybrid work model that balances flexibility with in-person collaboration. Employees based near our Santa Clara, Bangalore, and Noida offices are expected to work from the office three days per week, as regular in-person engagement is essential to how we build high-impact products and strong teams.