AI Enablement Engineer

About the role

Nearwater is building the internal capability to turn AI from a curiosity into a competitive advantage. As our AI Enablement Engineer, you will sit at the intersection of technology and the business, embedding with departments across the firm to understand how they work and then building working AI-powered solutions that change how they operate. This is not a pure engineering role and it is not a pure training role. It is the role that bridges both. You will prototype quickly, deploy practically, and teach others to do more themselves. You are equally comfortable in a conversation with a senior trader about what slows their process down and at a keyboard building a workflow that solves it. The goal is not to write production software. The goal is to make every team at Nearwater measurably better at their jobs through the intelligent use of AI. Today that means Claude, our current LLM, though our platform will continue to evolve to include other frontier and open-source models over time. What you learn from end users along the way will directly shape where the platform goes next.

Key facts

What you'll do

• Partner with business units to dissect workflows and pinpoint inefficiencies where AI can generate the highest leverage.
• Construct rapid prototypes using the Claude API, prompt engineering techniques, and automation tools to validate solutions in days rather than months.
• Engineer internal tools, templates, and integrations that allow departments to adopt AI capabilities with minimal friction.
• Architect reusable patterns and playbooks that codify successful approaches so that impact scales across teams and beyond initial pilots.
• Lead immersive workshops and pairing sessions that transform passive observers into confident, hands-on builders of AI-driven workflows.
• Craft prompt templates, skill libraries, and contextual how-to guides tailored to the specific problems and language of each team.
• Collaborate closely with the AI Enablement Specialist and department leads to sequence initiatives based on value, risk, and operational feasibility.
• Orchestrate practical demonstrations and training that support the rollout of new platform features firm-wide.
• Monitor and analyze adoption metrics, performance indicators, and user feedback to quantify the real impact of deployed solutions.
• Close the loop by transmitting insights from end users back to the product and platform teams to steer future development.
• Maintain a deep, operational fluency in the current platform built on Claude while tracking its expansion to incorporate other frontier and open-source models.
• Operate with a bias toward action, building fast and sharing freely to accelerate learning and organizational buy-in.
• Approach prototypes as disposable artifacts where the goal is validated learning and adoption, not polished code.
• Fluently switch between business dialogue and technical implementation, moving seamlessly from strategy discussions to script execution.
• Take ownership of outcomes and impact, focusing on the tangible improvement of team workflows rather than the mere delivery of project artifacts.

Requirements

• Bring 3+ years of experience from a technical background such as solutions engineering, technical product management, internal tools, or applied AI.
• Demonstrate hands-on experience building with LLM APIs, with a particular focus on Claude or OpenAI, including prompt engineering and agent design patterns.
• Write working code in Python or JavaScript at a prototyping level, understanding that the threshold for production engineering is not the target for this role.
• Exhibit strong instincts for workflow design and process improvement, translating ambiguous business problems into concrete technical solutions.
• Communicate with exceptional clarity to earn credibility and trust with both highly technical engineers and non-technical business leaders.
• Show familiarity with the Claude API, system prompts, and context engineering techniques as a distinct and valued advantage.
• Thrive in an environment where the pace of model releases and platform capabilities is rapid and continuous learning is mandatory.
• Operate effectively with incomplete information, making sound judgments based on limited data and iterative feedback.

Nice to have

• Preferred items are indicated within the requirements and description above, such as familiarity with Claude API, system prompts, and context engineering.

Practical notes

• Location is New York.
• Engagement details are to be confirmed from the source information.
• Compensation details are not provided in the source material.
• No information regarding hours, travel, visa, application pages, or deadlines is present in the source.