Staff AI Enablement Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Enablement Engineer based in United States. This is a high-impact, hands-on engineering role focused on defining how generative AI, automation, and agentic systems are adopted across an engineering organization.

You will build and operate practical AI-powered solutions that reduce engineering toil, improve productivity, and streamline operational workflows.

Working directly with engineering and product leaders, you will identify pain points, prioritize opportunities, and own an AI enablement roadmap from strategy through measurable execution.

You will evaluate emerging AI coding assistants and developer tools, separating meaningful capabilities from hype and guiding adoption based on evidence.

The role also offers the opportunity to modernize rules-based business systems using data-driven and machine-learning approaches.

You will establish scalable architectures, responsible-use guardrails, and measurement frameworks while helping engineering teams build confidence with AI.

This is an ideal opportunity for a staff-level engineer who combines strong technical depth with product thinking, organizational influence, and a passion for turning AI potential into working systems.

Accountabilities

• Serve as the engineering subject-matter expert for generative AI, agentic systems, AI-assisted development, and automation, staying current with rapidly evolving technologies and distinguishing durable capabilities from emerging hype.
• Meet with engineering teams to understand workflows, identify high-friction areas, and translate findings into a prioritized backlog of AI-enabled solutions.
• Own the AI enablement roadmap from strategy and leadership alignment through implementation, measurement, iteration, and communication of results.
• Evaluate, pilot, and guide adoption of AI coding assistants and agentic developer tools based on demonstrated value, organizational readiness, technical fit, cost, quality, and risk.
• Design, build, and operate a scalable network of specialized automation agents addressing high-friction engineering and operational workflows.
• Establish shared architectures and reusable patterns that allow new AI agents and automations to be developed and deployed efficiently and reliably.
• Instrument AI solutions and measure outcomes such as time saved, quality improvements, adoption, and operational efficiency, using real-world usage data to drive continuous improvement.
• Partner with teams across the organization to modernize rules-based and heuristic systems into more data-driven and ML-informed solutions.
• Collaborate with data and domain stakeholders to determine appropriate approaches to features, evaluation, model selection, and production implementation.
• Define practical AI adoption frameworks and guardrails covering quality, security, cost, responsible use, and operational reliability.
• Promote AI and automation best practices through documentation, demonstrations, workshops, brown-bag sessions, and hands-on collaboration with engineering teams.
• Clearly communicate technical tradeoffs—including build versus buy, cost, latency, quality, scalability, and risk—to both engineering teams and senior leadership.
• Act as a technical force multiplier by helping teams integrate effective AI practices into their everyday development workflows.

Requirements

• 8+ years of professional software engineering experience, with a demonstrated history of designing, building, and operating production systems end to end.
• Hands-on experience building production applications with large language models and agentic systems, including prompt engineering, context and tool design, evaluation, orchestration, and operationalization.
• Production-level experience using modern AI coding assistants and agentic development tools, combined with a clear perspective on when and how these technologies provide meaningful value.
• Working knowledge of applied machine learning fundamentals, including features, evaluation, model selection, and the transition from heuristic approaches to ML-based systems.
• Ability to drive technical adoption across an engineering organization without relying on formal authority, using credibility, communication, collaboration, and working software to influence change.
• Strong product instincts for int…