AI Engineering Lead

We are looking for an AI Engineering Lead to build and lead a team of 8-10 AI and full-stack engineers building the ProHance Agent, the agentic platform, and the intelligence layer that powers AI-first productivity governance products. This is a hands-on leadership role. You will work in the codebase, set technical direction, make architecture decisions, review critical work, and ship alongside your team while growing engineers, owning delivery, and raising the quality bar for how AI systems are built.

You will also help redefine how software is built in the wake of AI disruption. Engineering AI systems requires behavior to be specified, generated, evaluated, and refined rather than deterministically coded. The team is expected to serve as a lighthouse for the wider engineering organization by experimenting with new SDLC paradigms, proving what works, and codifying standards other teams can adopt. We are looking for a leader with a strong experimental mindset and a bias for execution, speed, and rapid iteration.

Responsibilities:

• Own the architecture of AI systems, including agentic workflows, multi-step reasoning, retrieval and grounding, tool use, and the LLM operations layer that keeps production AI healthy.
• Make key technical decisions on orchestration frameworks, model selection and routing, RAG design, and evaluation strategy.
• Stay hands-on by contributing to architecture, reviewing critical code and designs, and leading by example in the codebase.
• Ensure AI systems integrate cleanly into the platform with the security, access control, and privacy handling required for enterprise workforce data.
• Build, lead, and grow a high-performing team of 8-10 AI and full-stack engineers through hiring, mentoring, and development.
• Own end-to-end delivery for the team's charter, from AI features and the ProHance Agent to the full-stack surfaces that expose them, balancing speed, quality, and reliability.
• Set clear technical direction and priorities, and create conditions for engineers to do their best work with high ownership and fast feedback loops.
• Pioneer and operationalize new engineering paradigms for the AI era, including generator-evaluator loops, evaluation-driven development, intent expression, and loop engineering as first-class practices.
• Build tooling and conventions that make AI system development repeatable, where behavior is specified, generated, evaluated, and refined.
• Drive adoption of AI-assisted engineering across the team and turn successful approaches into standards, playbooks, and practices for the wider engineering organization.
• Act as an internal reference point for how AI changes software development by evaluating emerging techniques and tools and influencing engineering leadership beyond the team.
• Establish an operating model built for velocity, including rapid prototyping, tight ship-measure-learn cycles, and a willingness to stop what is not working.
• Run structured experiments to de-risk hard problems quickly and make evidence-based decisions on what to invest in and what to drop.
• Protect the team's ability to move fast without sacrificing the reliability and trust standards enterprise customers require.
• Establish the quality bar and release criteria for AI features, including evaluation harnesses, golden datasets, and regression suites that catch issues before customers do.
• Ensure production AI systems meet enterprise standards for scalability, availability, observability, auditability, and explainability.
• Build the trust architecture that allows enterprise buyers to interrogate and rely on AI outputs rather than treating the system as a black box.
• Partner with product, data science, and engineering leadership to translate strategy into an AI engineering roadmap and executable workflows.
• Represent the team's technical direction to senior stakeholders and bring AI-native engineering thinking into broader organizational decisions.

Requirements:

• 6-10 years of experience in software or AI engineering, with at least 2 years leading and growing engineering teams.
• Hands-on leadership experience with a track record of leading teams while staying technical, including coding and making architecture decisions.
• Demonstrable hands-on experience building and shipping production LLM or agentic AI systems, not just single-turn prompt-response applications.
• Experience with orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent.
• Strong understanding of RAG systems and evaluation discipline for AI applications.
• Ability to lead full-stack engineers credibly, with comfort across backend services, APIs, and modern application architecture.
• Hands-on experience building against modern LLMs such as OpenAI GPT, Anthropic Claude, Google Gemini, or equivalent.
• Strong Python skills for AI pipeline and orchestration work, along with working proficiency across the full-stack ecosystem.
• Proven history of shipping AI systems that work reliably in production, not just demos or prototypes.
• Demonstrated bias for action, experimentation, rapid iteration, and evidence-based decision-making under uncertainty.
• Curiosity and rigor in exploring AI-native engineering paradigms such as generator-evaluator loops, evaluation-driven development, and AI-assisted development practices, with the ability to codify them for others.

Nice To Have:

• Experience introducing AI-assisted engineering practices across a team or organization, with measurable adoption.
• Experience with enterprise-grade AI governance, including auditability, explainability, access control, and data privacy in regulated environments.
• Familiarity with workforce analytics, operational telemetry, or time-series data as a reasoning domain.
• Working knowledge of cloud platforms such as AWS, Azure, or GCP, along with Docker, Kubernetes, and CI/CD.
• Understanding of model routing, fine-tuning techniques such as LoRA and QLoRA, and inference optimization.
• Contributions to open-source LLM tooling, published work, or demonstrated thought leadership on AI systems engineering or AI-native software development.