Senior GenAI Engineering Lead — Planning & Execution (Python)

Location: Palo Alto; hybrid working model

Company

hirify.global is an AI company with 100+ employees building a platform that transforms core enterprise functions for Fortune 500 companies through code, statistical methods, guided ML, and LLM execution.

What you will do

• Own the roadmap, priorities, milestones, and delivery for the platform’s planning and execution layer.
• Lead the evolution of planner and executor architecture, including component boundaries, interfaces, and state management.
• Develop goal decomposition, agent and tool selection, dependency management, and adaptive replanning based on execution feedback.
• Build resilient, observable workflows with persistent state, failure recovery, human oversight, evaluations, and monitoring.
• Lead a small development team by setting technical direction, coordinating work, coaching developers, and maintaining engineering quality.
• Collaborate with product, domain, infrastructure, and security teams while writing and reviewing production Python code.

Requirements

• 8+ years of professional software engineering experience, including advanced Python proficiency.
• Experience owning a technical roadmap and/or leading a small development team to deliver software.
• Hands-on experience building production-level AI-powered workflows or agent systems.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Strong Python engineering skills, including API design, asynchronous programming, testing, and production debugging.
• Experience with LLM-based planning, tool calling, structured outputs, context management, and agent orchestration.
• Ability to design execution systems handling partial failures, retries, concurrency, and persistent state.
• Experience evaluating AI behavior and using traces, metrics, and logs to improve system performance.
• Experience with retrieval-augmented generation, structured and unstructured data integration, cloud-native services, databases, queues, and deployment practices.
• Ability to turn ambiguous requirements into a clear roadmap, communicate technical tradeoffs, collaborate across product and engineering teams, and balance experimentation with production reliability.

Culture & Benefits

• Hybrid working model.
• Opportunities to expand technical and leadership responsibilities.
• Direct ownership of core platform capabilities shaping AI delivery of professional services.
• Fast-moving startup environment with responsibility for balancing experimentation and production reliability.
• Compensation tailored to experience and skill set.